SENTIMENT ANALYSIS OF RESPONSES FROM BENEFICIARIES TO LIVELIHOOD SUBPROJECTS IN ZAMBIA

Author: Nathan Namatama
Institution: Leibniz Institute of Ecological Urban and Regional Development and Technical University of Dresden
Year: 2025
Related Publication: The effects and impacts of livelihood activities and unplanned human settlement growth on greenspace and wetland landscapes in Zambia: A case of the three areas of the Pilot Programme for Climate Resilience (PPCR)

Purpose of the Analysis

The analysis is conducted on the data collection that was conducted in Zambia from 23rd July 2024 to 22nd September 2024. The primary data was collected using ArcGIS Survey123 application in an offline mode mostly in certain areas that did not have access to internet while those that ahd internet an online mode was used. The respondents were interviewed using a semi structured question and the responses were recorede in the application as they were responding to the questions.

The analysis is done in the framework of systems thinking of looking at deep leverage points in the governance for transformation of Social Ecologocal Systems so as to attain sustainable transfomation. The analysis is done in both qualitative and quantitatives (descriptions) to produce graphs and tables that are visualised within the jupyterlab notebook.

Table of Contents

1. Importing Libraries
2. Reading the Excel Table
3. Missing values
4. Deleting Columnns
5. Renaming Columns
6. Selection of Likert Scale Columns
7. The Number of Responses
8. Defined or Responses with Choices
9. Description Statistics
10. Reasons
11. Specific Variables
12. Converting the Notebook

1. Importing Libraries

The liberarries that are needed for conducting the analysis are installed and downloaded. They are as follows:

• NLTK: For reading text and understanding it in a way that a human can do it
• Re: For text manipulation and pattern matching
• Pandas: For converting tables into a format understandable by the computer as well as visualisation
• Numpy: For conducting statistical culculations
• Matplotlib: For visualisation
• Seabron: For visualisation
• Io: For reading/writing binary and text data efficiently
• Csv: For reading the csv files
• Unicodeddata: For interacting with and analyzing Unicode characters
• String: For language analysis, user input, or file processing
• Plotly: For visualisation
• Plot_Likert: For visualisation of likert scales
• %matplotlib line: For visualisation within the jupyterlab notebook
• Nbconvert: For converting to HTML format
• WordCloud: For creating a word cloud
• Bigrams: For making words into pairs
• Trigrams: For making three words sequencies
• GridSpec For ploting a graph in a specific grid

import nltk
import re
from collections import Counter
from nltk.probability import FreqDist
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import sent_tokenize
from nltk.tokenize import word_tokenize
from nltk import sent_tokenize, word_tokenize, pos_tag
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import seaborn as sns
import io
from io import StringIO
import csv
import unicodedata
import string
import plotly
import plotly.express as px
import plot_likert
from sklearn.model_selection import train_test_split
%matplotlib inline
import nbconvert
from nbconvert import HTMLExporter
import nbformat
from wordcloud import WordCloud
from nltk import bigrams
from nltk import trigrams
import subprocess
import shutil
import pypandoc

nltk.download('punkt_tab')
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')

[nltk_data] Downloading package punkt_tab to
[nltk_data]     C:\Users\nazin\AppData\Roaming\nltk_data...
[nltk_data]   Package punkt_tab is already up-to-date!
[nltk_data] Downloading package punkt to
[nltk_data]     C:\Users\nazin\AppData\Roaming\nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package stopwords to
[nltk_data]     C:\Users\nazin\AppData\Roaming\nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
[nltk_data] Downloading package wordnet to
[nltk_data]     C:\Users\nazin\AppData\Roaming\nltk_data...
[nltk_data]   Package wordnet is already up-to-date!

True

2. Reading the Excel Table

The csv file is converted to a padas dataframe
The dataframe table is displyed with ALL columns and rows with cutting any

df = pd.read_csv(r"D:\DataAnalysis\Social_Survey_Questionnaire_for_Beneficiaries_0.csv")
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_rows', None)  
pd.set_option('display.max_columns', None) 

type(df)

pandas.core.frame.DataFrame

#df

df.shape

(150, 120)

3. Missing values

The Pandas Dataframe is checked for mising values

#df.isnull().sum()

3.1 Dropping all the Missing Values

#df.isnull().sum().sort_values(ascending=False)

#df.dropna(inplace=True)

4. Deleting Columnns

There are 120 columns and 85 columns were removed from the pandas dataframe so as to focus on specific columns that have questtions in regards to livelihood and landscape transfromation

df1=df.drop(df.columns[[1,2,3,4,5,6,7,8,9,10,11,12,13,14,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,116,117,118,119]], axis = 1)

#df1

5. Renaming Columns

The headings of the columns were renamed for easy inclusion in the code

df1.columns

Index(['ObjectID', '6. Ward Name', '7. Sub Project Name',
       '8. Type of Livelihood', '9. Size (Lima)', '12. Name of main project',
       '13. Do you represent other beneficiaries?',
       '14. How many beneficiaries do you represent?',
       '45. Are there cultural practices that hinder the sustainable management of Forests, Wetlands, National Game Parks and Biodiversity?',
       '46. What reasons can you give for your answer above?',
       '47. Do you think some cultural practices can be changed?',
       '48. Do you consider cultural aspects when formulating the livelihood projects?',
       '49. What reasons can you give for your answer above?',
       '50. What is the main purpose of landscapes (Forests, Water Bodies, Wetlands… etc.) in your livelihood?',
       '51. Do you think there is need to measure indicators when managing landscapes?',
       '52. How is your connection to nature like?',
       '53. What reasons can you give for your answer above?',
       '54. How long have you worked on this livelihood project?',
       '55. Does your livelihood depend on the natural resources for a living?',
       '56. Do you consider changing your livelihood strategy in future?',
       '57. Do you think it is easier to change your livelihood practices?',
       '58. Are you able to give reasons for your answer above in your ability to change your livelihood practices?',
       '59. Have the ecosystem services reduced from the inception of the project in your ward?',
       '60. Has the deforestation increased in the ward?',
       '61. Do you think protected areas are a hindrance to your livelihoods?',
       '62. Are there new livelihood projects that you think of that have never been implemented?',
       '63. Do you think the livelihood subprojects are contributing to the sustainability of landscapes?',
       '64. Which livelihood is a major contributor to landscape transformation?',
       '65. What reasons can you give for your answer above?',
       '66. Which type of landscape do you depend on much for a livelihood?',
       '67. What reasons can you give for your answer above?', 'Specify:.4',
       'Specify:.5', 'Specify:.6', 'Specify:.7'],
      dtype='object')

df2=df1.rename(columns= {'1. Do you agree to take part in the above study?': 'Part_study',
                        '2. Do you know that your participation is voluntary and you are free to withdraw anytime?':'Participation_Voluntary', 
                        '3. Do you give permission to the data that emerges to be used by the researchers only in an anonymised form?': 'Anonymised_Form', 
                        '5. Date': 'Date', 
                        '6. Ward Name': 'Ward_Name', 
                        '7. Sub Project Name': 'Sub_Project_name', 
                        '8. Type of Livelihood': 'Livelihood', 
                        '9. Size (Lima)': 'Size', 
                        '12. Name of main project': 'Name_Main_Project',
                        '13. Do you represent other beneficiaries?': 'Representing_Others',
                        '14. How many beneficiaries do you represent?': 'Number_Beneficiaries',
                        '45. Are there cultural practices that hinder the sustainable management of Forests, Wetlands, National Game Parks and Biodiversity?': 'Cultural_Practices_Hinder', 
                        '46. What reasons can you give for your answer above?': 'Cultural_Practices_Hinder_Reason', 
                        '47. Do you think some cultural practices can be changed?': 'Cultural_Practices_Changed', 
                        '48. Do you consider cultural aspects when formulating the livelihood projects?': 'Cultural_Aspects_Considered',
                        '49. What reasons can you give for your answer above?': 'Cultural_Aspects_Considered_Reasons', 
                        '50. What is the main purpose of landscapes (Forests, Water Bodies, Wetlands… etc.) in your livelihood?': 'Purpose_Landscape', 
                        '51. Do you think there is need to measure indicators when managing landscapes?': 'Measure_Indicators', 
                        '52. How is your connection to nature like?': 'Connection_Nature', 
                        '53. What reasons can you give for your answer above?': 'Connection_Nature_Reasons',
                       '54. How long have you worked on this livelihood project?': 'Range_Years',
                       '55. Does your livelihood depend on the natural resources for a living?': 'Livilihood_Depenedent',
                       '56. Do you consider changing your livelihood strategy in future?': 'Change_Livelihood',
                       '57. Do you think it is easier to change your livelihood practices?': 'Change_Livelihood_Easy',
                       '58. Are you able to give reasons for your answer above in your ability to change your livelihood practices?': 'Change_Livelihood_Easy_Reasons',
                       '59. Have the ecosystem services reduced from the inception of the project in your ward?': 'Ecosystem_Services_Reduced',
                       '60. Has the deforestation increased in the ward?': 'Deforestaion_Increased',
                       '61. Do you think protected areas are a hindrance to your livelihoods?': 'Protected_Areas_Hinderarnce_Livelihood',
                       '62. Are there new livelihood projects that you think of that have never been implemented?': 'New_Livelihood_Projects',
                       '63. Do you think the livelihood subprojects are contributing to the sustainability of landscapes?': 'Subprojects_Sustainability_Contribution',
                       '64. Which livelihood is a major contributor to landscape transformation?': 'Contributor_Landscape_Transformation',
                       '65. What reasons can you give for your answer above?': 'Contributor_Landscape_Transformation_Reasons',
                       '66. Which type of landscape do you depend on much for a livelihood?': 'Landscape_Depended_Livelihood',
                       '67. What reasons can you give for your answer above?': 'Landscape_Depeneded_Livelihood_Reasons',
                       'Specify:.4': 'Purpose_Landscape_Specific',
                       'Specify:.5': 'Connection_Nature_Specific',
                       'Specify:.6': 'Contributor_Landscape_Transformation_Specific',
                       'Specify:.7': 'Landscape_Depended_Livelihood_Specific'})

#df2

6. Selection of Likert Scale Columns

The columns that had likert scale responses were group in a single dataframe and they are 12 in number the columns were looking at the following questions as numbered in the questionaire:

• '45. Are there cultural practices that hinder the sustainable management of Forests, Wetlands, National Game Parks and Biodiversity?
• '47. Do you think some cultural practices can be changed?',
• '48. Do you consider cultural aspects when formulating the livelihood projects?',
• '51. Do you think there is need to measure indicators when managing landscapes?',
• '55. Does your livelihood depend on the natural resources for a living?',
• '56. Do you consider changing your livelihood strategy in future?',
• '57. Do you think it is easier to change your livelihood practices?',
• '59. Have the ecosystem services reduced from the inception of the project in your ward?',
• '60. Has the deforestation increased in the ward?',
• '61. Do you think protected areas are a hindrance to your livelihoods?',
• '62. Are there new livelihood projects that you think of that have never been implemented?',
• '63. Do you think the livelihood subprojects are contributing to the sustainability of landscapes?'

df3=df2.drop(df2.columns[[0,1,2,3,4,5,6,7,9,12,13,15,16,17,21,27,28,29,30,31,32,33,34]], axis = 1)

#df3

7. The Number of Responses

The number of responses were counted for each column that had a likert scale and the NaN indicates no response

all_counts = df3.apply(pd.Series.value_counts, dropna=False)

all_counts

	Cultural_Practices_Hinder	Cultural_Practices_Changed	Cultural_Aspects_Considered	Measure_Indicators	Livilihood_Depenedent	Change_Livelihood	Change_Livelihood_Easy	Ecosystem_Services_Reduced	Deforestaion_Increased	Protected_Areas_Hinderarnce_Livelihood	New_Livelihood_Projects	Subprojects_Sustainability_Contribution
Agree_Likert	20	29	19	28	35	39	46	39	16	14.0	58	37
Disagree_Likert	22	22	20	4	25	37	24	13	32	19.0	15	4
Strongly_Agree_Likert	21	29	22	81	44	38	49	72	72	15.0	36	81
Strongly_Disagree_Likert	70	41	66	22	32	20	18	16	17	83.0	19	15
Undecided_Likert	4	9	7	7	4	5	4	3	1	NaN	10	3
NaN	13	20	16	8	10	11	9	7	12	19.0	12	10

7.1 Transposing the Dataframe Table

The columns and the rows were interchanged so that they can be easily presented on the graph

all_counts1 = all_counts.head().T

all_counts1

	Agree_Likert	Disagree_Likert	Strongly_Agree_Likert	Strongly_Disagree_Likert	Undecided_Likert
Cultural_Practices_Hinder	20.0	22.0	21.0	70.0	4.0
Cultural_Practices_Changed	29.0	22.0	29.0	41.0	9.0
Cultural_Aspects_Considered	19.0	20.0	22.0	66.0	7.0
Measure_Indicators	28.0	4.0	81.0	22.0	7.0
Livilihood_Depenedent	35.0	25.0	44.0	32.0	4.0
Change_Livelihood	39.0	37.0	38.0	20.0	5.0
Change_Livelihood_Easy	46.0	24.0	49.0	18.0	4.0
Ecosystem_Services_Reduced	39.0	13.0	72.0	16.0	3.0
Deforestaion_Increased	16.0	32.0	72.0	17.0	1.0
Protected_Areas_Hinderarnce_Livelihood	14.0	19.0	15.0	83.0	NaN
New_Livelihood_Projects	58.0	15.0	36.0	19.0	10.0
Subprojects_Sustainability_Contribution	37.0	4.0	81.0	15.0	3.0

7.2 Changing the Order of Columns

The order of columns was changed so that they can be easily analysed

all_counts2 = all_counts1.iloc[:, [3, 1, 4, 0, 2]]

all_counts2 

	Strongly_Disagree_Likert	Disagree_Likert	Undecided_Likert	Agree_Likert	Strongly_Agree_Likert
Cultural_Practices_Hinder	70.0	22.0	4.0	20.0	21.0
Cultural_Practices_Changed	41.0	22.0	9.0	29.0	29.0
Cultural_Aspects_Considered	66.0	20.0	7.0	19.0	22.0
Measure_Indicators	22.0	4.0	7.0	28.0	81.0
Livilihood_Depenedent	32.0	25.0	4.0	35.0	44.0
Change_Livelihood	20.0	37.0	5.0	39.0	38.0
Change_Livelihood_Easy	18.0	24.0	4.0	46.0	49.0
Ecosystem_Services_Reduced	16.0	13.0	3.0	39.0	72.0
Deforestaion_Increased	17.0	32.0	1.0	16.0	72.0
Protected_Areas_Hinderarnce_Livelihood	83.0	19.0	NaN	14.0	15.0
New_Livelihood_Projects	19.0	15.0	10.0	58.0	36.0
Subprojects_Sustainability_Contribution	15.0	4.0	3.0	37.0	81.0

7.3 Visualising the Results

The results were visualised in form of number of responses

%matplotlib inline
plot_likert.plot_counts(all_counts2, plot_likert.scales.agree,  plot_percentage=False, bar_labels=True, bar_labels_color="snow", colors=plot_likert.colors.default_with_darker_neutral)
plt.title("Figure 1: The Total Number of Responses to Variables on Sustainable Transformation", fontsize=14)
plt.show()

C:\Users\nazin\AppData\Local\anaconda3\envs\NLTK_Py_3_12\Lib\site-packages\plot_likert\plot_likert.py:101: FutureWarning: parameter `plot_percentage` for `plot_likert.likert_counts` is deprecated, set it to None and use `compute_percentages` instead
  warn(

7.4 Converting the Responses to Percentages

The responses were converted to percentages

all_counts3 = df3.apply(lambda col: col.value_counts(normalize=True, dropna=False).round(2))

all_counts3 

	Cultural_Practices_Hinder	Cultural_Practices_Changed	Cultural_Aspects_Considered	Measure_Indicators	Livilihood_Depenedent	Change_Livelihood	Change_Livelihood_Easy	Ecosystem_Services_Reduced	Deforestaion_Increased	Protected_Areas_Hinderarnce_Livelihood	New_Livelihood_Projects	Subprojects_Sustainability_Contribution
Agree_Likert	0.13	0.19	0.13	0.19	0.23	0.26	0.31	0.26	0.11	0.09	0.39	0.25
Disagree_Likert	0.15	0.15	0.13	0.03	0.17	0.25	0.16	0.09	0.21	0.13	0.10	0.03
Strongly_Agree_Likert	0.14	0.19	0.15	0.54	0.29	0.25	0.33	0.48	0.48	0.10	0.24	0.54
Strongly_Disagree_Likert	0.47	0.27	0.44	0.15	0.21	0.13	0.12	0.11	0.11	0.55	0.13	0.10
Undecided_Likert	0.03	0.06	0.05	0.05	0.03	0.03	0.03	0.02	0.01	NaN	0.07	0.02
NaN	0.09	0.13	0.11	0.05	0.07	0.07	0.06	0.05	0.08	0.13	0.08	0.07

7.4.1 Transposing the Dataframe Table

  Interchanging the rows and columns

all_counts4 = all_counts3.head().T

all_counts4

	Agree_Likert	Disagree_Likert	Strongly_Agree_Likert	Strongly_Disagree_Likert	Undecided_Likert
Cultural_Practices_Hinder	0.13	0.15	0.14	0.47	0.03
Cultural_Practices_Changed	0.19	0.15	0.19	0.27	0.06
Cultural_Aspects_Considered	0.13	0.13	0.15	0.44	0.05
Measure_Indicators	0.19	0.03	0.54	0.15	0.05
Livilihood_Depenedent	0.23	0.17	0.29	0.21	0.03
Change_Livelihood	0.26	0.25	0.25	0.13	0.03
Change_Livelihood_Easy	0.31	0.16	0.33	0.12	0.03
Ecosystem_Services_Reduced	0.26	0.09	0.48	0.11	0.02
Deforestaion_Increased	0.11	0.21	0.48	0.11	0.01
Protected_Areas_Hinderarnce_Livelihood	0.09	0.13	0.10	0.55	NaN
New_Livelihood_Projects	0.39	0.10	0.24	0.13	0.07
Subprojects_Sustainability_Contribution	0.25	0.03	0.54	0.10	0.02

7.4.2 Changing the Order of Columns

  The order of columns was changed

all_counts5 = all_counts4.iloc[:, [3, 1, 4, 0, 2]]

all_counts5 

	Strongly_Disagree_Likert	Disagree_Likert	Undecided_Likert	Agree_Likert	Strongly_Agree_Likert
Cultural_Practices_Hinder	0.47	0.15	0.03	0.13	0.14
Cultural_Practices_Changed	0.27	0.15	0.06	0.19	0.19
Cultural_Aspects_Considered	0.44	0.13	0.05	0.13	0.15
Measure_Indicators	0.15	0.03	0.05	0.19	0.54
Livilihood_Depenedent	0.21	0.17	0.03	0.23	0.29
Change_Livelihood	0.13	0.25	0.03	0.26	0.25
Change_Livelihood_Easy	0.12	0.16	0.03	0.31	0.33
Ecosystem_Services_Reduced	0.11	0.09	0.02	0.26	0.48
Deforestaion_Increased	0.11	0.21	0.01	0.11	0.48
Protected_Areas_Hinderarnce_Livelihood	0.55	0.13	NaN	0.09	0.10
New_Livelihood_Projects	0.13	0.10	0.07	0.39	0.24
Subprojects_Sustainability_Contribution	0.10	0.03	0.02	0.25	0.54

7.4.3 Visualising the Results

The results are visualised as pecentages

%matplotlib inline
plot_likert.plot_counts(all_counts5, plot_likert.scales.agree,  plot_percentage=True, figsize=(16, 7), bar_labels=True, bar_labels_color="snow", colors=plot_likert.colors.default_with_darker_neutral)
plt.title("Figure 2: The Percentage Number of Responses to Variables on Sustainable Transformation", fontsize=18)
plt.savefig("Likertscale.jpg")
plt.savefig("Likertscale1.png", dpi=300)
plt.show()

C:\Users\nazin\AppData\Local\anaconda3\envs\NLTK_Py_3_12\Lib\site-packages\plot_likert\plot_likert.py:101: FutureWarning: parameter `plot_percentage` for `plot_likert.likert_counts` is deprecated, set it to None and use `compute_percentages` instead
  warn(

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8. Defined or Responses with Choices

The responses that had choices other than those with a likert scale were grouped in a pandas dataframe looking the following question:

1. '50. What is the main purpose of landscapes (Forests, Water Bodies, Wetlands… etc.) in your livelihood?': 'Purpose_Landscape',
2. '52. How is your connection to nature like?': 'Connection_Nature',
3. '54. How long have you worked on this livelihood project?': 'Range_Years',
4. '64. Which livelihood is a major contributor to landscape transformation?': 'Contributor_Landscape_Transformation',
5. '66. Which type of landscape do you depend on much for a livelihood?': 'Landscape_Depended_Livelihood'

df4=df2.drop(df2.columns[[0,1,2,3,4,5,6,7,8,9,10,11,12,14,16,18,19,20,21,22,23,24,25,26,28,30,31,32,33,34]], axis = 1)

#df4

8.1 The Purpose of Landscape to People

PL = df4['Purpose_Landscape'].value_counts(dropna=False)
PL_P = (df4['Purpose_Landscape'].value_counts(normalize=True, dropna=False).round(2))

PL_P

Purpose_Landscape
Nature_Protection_Purpose                              0.62
NaN                                                    0.22
Source_Income_Purpose                                  0.11
Nature_Protection_Purpose,Other_Purpose                0.01
Nature_Protection_Purpose,Ancestral_Shrines_Purpose    0.01
Source_Income_Purpose,Nature_Protection_Purpose        0.01
Nature_Protection_Purpose,Source_Income_Purpose        0.01
Source_Income_Purpose,Other_Purpose                    0.01
No_Idea_Purpose                                        0.01
Other_Purpose                                          0.01
Name: proportion, dtype: float64

df_PL = pd.DataFrame(PL)

df_PL

	count
Purpose_Landscape
Nature_Protection_Purpose	93
NaN	33
Source_Income_Purpose	16
Nature_Protection_Purpose,Other_Purpose	2
Nature_Protection_Purpose,Ancestral_Shrines_Purpose	1
Source_Income_Purpose,Nature_Protection_Purpose	1
Nature_Protection_Purpose,Source_Income_Purpose	1
Source_Income_Purpose,Other_Purpose	1
No_Idea_Purpose	1
Other_Purpose	1

ax = sns.countplot(df4["Purpose_Landscape"])
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
    ax.set_title("Figure 3: Number of Responses on the Purpose of the Landscape", fontsize=14)
plt.show()

8.2 The Connection of Nature to People

CN = df4['Connection_Nature'].value_counts(dropna=False)
CN_P = (df4['Connection_Nature'].value_counts(normalize=True, dropna=False).round(2))

CN_P

Connection_Nature
Material_Connection                                  0.82
NaN                                                  0.09
Other_Connection                                     0.04
Experiential_Connection                              0.01
Philosophical_Connection,Psychological_Connection    0.01
Psychological_Connection,Material_Connection         0.01
Psychological_Connection                             0.01
Philosophical_Connection                             0.01
Experiential_Connection,Material_Connection          0.01
Name: proportion, dtype: float64

df_CN = pd.DataFrame(CN)

df_CN

	count
Connection_Nature
Material_Connection	123
NaN	13
Other_Connection	6
Experiential_Connection	2
Philosophical_Connection,Psychological_Connection	2
Psychological_Connection,Material_Connection	1
Psychological_Connection	1
Philosophical_Connection	1
Experiential_Connection,Material_Connection	1

ax = sns.countplot(df4["Connection_Nature"])
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
    ax.set_title("Figure 4: The Number of Responses to Connection to Nature", fontsize=14)
plt.show()

agreement_levels = ["Material_Connection", "Other_Connection"]
CN_R = df2[df2["Connection_Nature"].isin(agreement_levels)]
CN_R1 = CN_R.drop(CN_R.columns[[0,1,2,3,4,6,7,8,9,10,11,12,13,14,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]], axis = 1)
CN_R1grouped = CN_R1.groupby('Name_Main_Project')['Connection_Nature']
#CN_R1

lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in CN_R1.iterrows():
    CN_R1_filter_sentence = []
    CN_R1_sentence = row["Connection_Nature_Reasons"]
    if pd.isnull(CN_R1_sentence):
       continue
    CN_R1_sentence_cleaned = re.sub(r'[^\w\s]','',CN_R1_sentence)
    CN_R1_words = nltk.word_tokenize(CN_R1_sentence_cleaned)
    CN_R1_words = [lemmatizer.lemmatize(w) for w in CN_R1_words if w.lower() not in stop_words]
    CN_R1_filter_sentence.extend(CN_R1_words)
    print(CN_R1_filter_sentence)

['use', 'income']
['source', 'income', 'future', 'generation', 'see']
['Source', 'income']
['source', 'income']
['Thats', 'get', 'income', 'well', 'example', 'cattle', 'use', 'cultivating']
['game', 'park', 'help', 'u', 'source', 'income', 'form', 'meat', 'animal', 'tree', 'rain', 'come', 'protected']
['need', 'keep', 'fish', 'instance', 'fish', 'pond', 'get', 'extinct']
['one', 'look', 'daily', 'basis']
['source', 'livelihood']
['need', 'protect', 'nature', 'instance', 'cutting', 'tree', 'along', 'river', 'lead', 'drying', 'destruction', 'animal', 'biodiversity', 'river']
['answer']
['purpose', 'future', 'meet', 'need']
['source', 'income']
['tree', 'help', 'u', 'bring', 'rainfall']
['Source', 'income']
['harvest', 'right', 'time', 'prevent', 'management', 'natural', 'resource', 'properly']
['taking', 'care']
['Source', 'income']
['Source', 'income']
['bring', 'development', 'tourism']
['income']
['natural', 'resource', 'like', 'tree', 'give', 'shade', 'medicine', 'also', 'get', 'fresh', 'air']
['Like', 'water', 'source', 'life', 'tree', 'source', 'fresh', 'water']
['source', 'income']
['source', 'income']
['ZAWA', 'Officers', 'one', 'connected', 'look']
['source', 'income', 'tourism']
['answer']
['help', 'u', 'source', 'food', 'well', 'water']
['tree', 'protected', 'lead', 'rainfall', 'area']
['source', 'livelihood']
['Somehow', 'protect']
['help', 'lot', 'thing', 'air', 'breath']
['protect', 'u', 'instance', 'tree', 'protect', 'wind', 'bring', 'fresh', 'air', 'well', 'prevent', 'river', 'drying']
['Thats', 'get', 'honey', 'bee', 'hive', 'u', 'lot', 'money']
['Thats', 'get', 'free', 'air', 'traditional', 'medicine', 'livelihood', 'depend', 'natural', 'resource']
['instance', 'fish', 'caught', 'brings', 'income', 'source', 'food', 'well', 'tree', 'bring', 'rainfall', 'indirectly']
[]

CN_R1["Connection_Nature_Reasons"] = CN_R1["Connection_Nature_Reasons"].fillna("")  
CN_R1["Connection_Nature_Reasons"] = CN_R1["Connection_Nature_Reasons"].astype(str)
CN_R1_Text = " ".join(CN_R1["Connection_Nature_Reasons"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(CN_R1_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Connection Nature", loc="left", fontsize=20, pad=20)
plt.axis("off")  
plt.show()

CN_R1_bigrams_list = list(CN_R1_filter_sentence)
print(CN_R1_bigrams_list)
#CN_R1_bigram_counts = Counter(zip(bigrams_list, CN_R1_bigrams_list[1:]))
#print(CN_R1_bigram_counts)  
#CN_R1_bigrams = pd.DataFrame(CN_R1_bigram_counts.most_common(7),
#columns = ['Word', 'Frequency'])
#print(CN_R1_bigrams)

[]

8.3 The Length of Existence of the Livelihood Project

RY_grouped = df2.groupby('Name_Main_Project')['Range_Years'].value_counts(dropna=False)

RY_grouped

Name_Main_Project                     Range_Years           
EbA_CENTRAL_MUCHINGA_LUAPULA          3to4Years_Long             2
                                      NaN                        1
Ecosystem Conservation_NORTH_WESTERN  3to4Years_Long             8
                                      NaN                        2
                                      7to8Years_Long             1
PIN_WESTERN                           3to4Years_Long             5
                                      NaN                        1
SCRALA_SOUTHERN_WESTERN_NORTHEN       3to4Years_Long            12
                                      5to6Years_Long             9
                                      Lessthan2Years_Long        6
                                      Greaterthan9Years_Long     2
                                      7to8Years_Long             1
SCReBS_WESTERN                        5to6Years_Long             5
                                      7to8Years_Long             5
                                      Greaterthan9Years_Long     3
                                      3to4Years_Long             2
SCRiKA_LS                             5to6Years_Long            31
                                      3to4Years_Long             6
                                      NaN                        5
                                      7to8Years_Long             2
TRALARD_LNM                           3to4Years_Long            28
                                      5to6Years_Long             5
                                      Lessthan2Years_Long        3
                                      Greaterthan9Years_Long     2
                                      NaN                        2
Name: count, dtype: int64

RY_grouped1 = pd.DataFrame(RY_grouped)

RY_grouped1

		count
Name_Main_Project	Range_Years
EbA_CENTRAL_MUCHINGA_LUAPULA	3to4Years_Long	2
	NaN	1
Ecosystem Conservation_NORTH_WESTERN	3to4Years_Long	8
	NaN	2
	7to8Years_Long	1
PIN_WESTERN	3to4Years_Long	5
	NaN	1
SCRALA_SOUTHERN_WESTERN_NORTHEN	3to4Years_Long	12
	5to6Years_Long	9
	Lessthan2Years_Long	6
	Greaterthan9Years_Long	2
	7to8Years_Long	1
SCReBS_WESTERN	5to6Years_Long	5
	7to8Years_Long	5
	Greaterthan9Years_Long	3
	3to4Years_Long	2
SCRiKA_LS	5to6Years_Long	31
	3to4Years_Long	6
	NaN	5
	7to8Years_Long	2
TRALARD_LNM	3to4Years_Long	28
	5to6Years_Long	5
	Lessthan2Years_Long	3
	Greaterthan9Years_Long	2
	NaN	2

plt.figure(figsize=(8.7, 8.27))
hue_order = ["Lessthan2Years_Long", "3to4Years_Long", "5to6Years_Long", "7to8Years_Long", "Greaterthan9Years_Long", "NaN"]
ax = sns.barplot(data = RY_grouped1, x="count", y="Name_Main_Project", hue="Range_Years", hue_order=hue_order, legend=True)
ax.set_title("Figure 5b: The Number of Livelihood Projects in a Particular Range of Year", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()

LP = df4['Range_Years'].value_counts(dropna=False)
LP_P = (df4['Range_Years'].value_counts(normalize=True, dropna=False).round(2))

LP_P

Range_Years
3to4Years_Long            0.42
5to6Years_Long            0.33
NaN                       0.08
Lessthan2Years_Long       0.06
7to8Years_Long            0.06
Greaterthan9Years_Long    0.05
Name: proportion, dtype: float64

df_LP = pd.DataFrame(LP)

df_LP

	count
Range_Years
3to4Years_Long	63
5to6Years_Long	50
NaN	12
Lessthan2Years_Long	9
7to8Years_Long	9
Greaterthan9Years_Long	7

ax = sns.countplot(df4["Range_Years"])
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
    ax.set_title("Figure 5b: The Number of Livelihood Projects in a Particular Range of Year", fontsize=14)
plt.show()

8.4 Major Contributor to Landscape Transformation

CLT_grouped = df2.groupby('Name_Main_Project')['Contributor_Landscape_Transformation'].value_counts(dropna=False)

CLT_grouped

Name_Main_Project                     Contributor_Landscape_Transformation
EbA_CENTRAL_MUCHINGA_LUAPULA          Land_Agriculture                         3
Ecosystem Conservation_NORTH_WESTERN  Land_Agriculture                         3
                                      Uncontrolled_Fires                       3
                                      NaN                                      3
                                      Uncontrolled_Fires,Land_Agriculture      1
                                      Wood_Extraction                          1
PIN_WESTERN                           Uncontrolled_Fires                       3
                                      Wood_Extraction                          2
                                      NaN                                      1
SCRALA_SOUTHERN_WESTERN_NORTHEN       Uncontrolled_Fires                      19
                                      Wood_Extraction                          7
                                      Land_Agriculture                         2
                                      NaN                                      2
SCReBS_WESTERN                        Uncontrolled_Fires                       7
                                      Wood_Extraction                          5
                                      Land_Agriculture                         2
                                      Other                                    1
SCRiKA_LS                             Uncontrolled_Fires                      13
                                      Land_Agriculture                        12
                                      Wood_Extraction                         11
                                      NaN                                      6
                                      Uncontrolled_Grazing                     2
TRALARD_LNM                           Land_Agriculture                        22
                                      Wood_Extraction                         13
                                      Uncontrolled_Fires                       4
                                      NaN                                      1
Name: count, dtype: int64

CLT_grouped1 = pd.DataFrame(CLT_grouped)

CLT_grouped1

		count
Name_Main_Project	Contributor_Landscape_Transformation
EbA_CENTRAL_MUCHINGA_LUAPULA	Land_Agriculture	3
Ecosystem Conservation_NORTH_WESTERN	Land_Agriculture	3
	Uncontrolled_Fires	3
	NaN	3
	Uncontrolled_Fires,Land_Agriculture	1
	Wood_Extraction	1
PIN_WESTERN	Uncontrolled_Fires	3
	Wood_Extraction	2
	NaN	1
SCRALA_SOUTHERN_WESTERN_NORTHEN	Uncontrolled_Fires	19
	Wood_Extraction	7
	Land_Agriculture	2
	NaN	2
SCReBS_WESTERN	Uncontrolled_Fires	7
	Wood_Extraction	5
	Land_Agriculture	2
	Other	1
SCRiKA_LS	Uncontrolled_Fires	13
	Land_Agriculture	12
	Wood_Extraction	11
	NaN	6
	Uncontrolled_Grazing	2
TRALARD_LNM	Land_Agriculture	22
	Wood_Extraction	13
	Uncontrolled_Fires	4
	NaN	1

plt.figure(figsize=(8.7, 8.27))
hue_order = ["Land_Agriculture", "Wood_Extraction", "Uncontrolled_Fires", "Uncontrolled_Grazing ", "Other", "NaN"]
ax = sns.barplot(data = CLT_grouped1, x="count", y="Name_Main_Project", hue="Contributor_Landscape_Transformation", hue_order=hue_order, legend=True)
ax.set_title("Figure 6a: Major Contributor to Landscape Transformation", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()

CLT = df4['Contributor_Landscape_Transformation'].value_counts(dropna=False)
CLT_P = (df4['Contributor_Landscape_Transformation'].value_counts(normalize=True, dropna=False).round(2))

CLT_P

Contributor_Landscape_Transformation
Uncontrolled_Fires                     0.33
Land_Agriculture                       0.29
Wood_Extraction                        0.26
NaN                                    0.09
Uncontrolled_Grazing                   0.01
Uncontrolled_Fires,Land_Agriculture    0.01
Other                                  0.01
Name: proportion, dtype: float64

df_CLT = pd.DataFrame(CLT)

df_CLT

	count
Contributor_Landscape_Transformation
Uncontrolled_Fires	49
Land_Agriculture	44
Wood_Extraction	39
NaN	14
Uncontrolled_Grazing	2
Uncontrolled_Fires,Land_Agriculture	1
Other	1

ax = sns.countplot(df4["Contributor_Landscape_Transformation"])
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
    ax.set_title("Figure 6b: Major Contributor to Landscape Transformation", fontsize=14)
plt.show()

agreement_levels = ["Wood_Extration", "Land_Agriculture","Uncontrolled_Fires"]
CLT_R = df2[df2["Contributor_Landscape_Transformation"].isin(agreement_levels)]
CLT_R1 = CN_R.drop(CLT_R.columns[[0,1,2,3,4,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,29,30,31,32,33,34]], axis = 1)
CLT_R1grouped = CLT_R1.groupby('Name_Main_Project')['Contributor_Landscape_Transformation']
#CLT_R1

lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in CLT_R1.iterrows():
    CLT_R1_filter_sentence = []
    CLT_R1_sentence = row["Contributor_Landscape_Transformation_Reasons"]
    if pd.isnull(CLT_R1_sentence):
       continue
    CLT_R1_sentence_cleaned = re.sub(r'[^\w\s]','',CLT_R1_sentence)
    CLT_R1_words = nltk.word_tokenize(CLT_R1_sentence_cleaned)
    CLT_R1_words = [lemmatizer.lemmatize(w) for w in CLT_R1_words if w.lower() not in stop_words]
    CLT_R1_filter_sentence.extend(CLT_R1_words)
    print(CLT_R1_filter_sentence)

['requires', 'huge', 'land', 'others', 'like', 'making', 'charcoal']
['burning', 'charcoal', 'give', 'smoke', 'destroys', 'ozone', 'layer', 'well', 'brings', 'acidic', 'rain']
['agriculture', 'activity', 'food']
['electricity', 'thus', 'huge', 'demand', 'energy']
['charcoal', 'purpose', 'bad', 'land', 'left', 'bare', 'unlike', 'agriculture', 'cutting', 'replaced', 'plant']
['cultivation', 'cassava', 'requires', 'always', 'barren', 'land', 'never', 'cultivated', 'grow', 'well', 'crop', 'bean', 'vegetable', 'maize', 'reduce', 'deforestation']
['burn', 'tree', 'shoot']
['Chitemene', 'system', 'cultivation', 'lead', 'deforestation', 'others']
['source', 'food', 'people', 'area']
['Thats', 'get', 'livelihood']
['source', 'income']
['time', 'cut', 'tree', 'even', 'want', 'cultivate', 'well', 'burining', 'kill', 'animal', 'necesary', 'making', 'soil', 'fertile']
['Buring', 'destroy', 'product', 'crop', 'soil']
['cutting', 'treed', 'change', 'landscape']
['cutting', 'tress', 'destroys', 'much', 'regrowth']
['u', 'income', 'huge', 'area', 'tree', 'cut']
['commercial', 'agriculture', 'activity', 'clear', 'huge', 'chuck', 'land']
['Source', 'income']
['forming', 'og', 'food', 'crop']
['source', 'income']
['people', 'plant', 'huge', 'area', 'land', 'livelihood']
['animal', 'food', 'well', 'tree', 'would', 'dry']
['income', 'food', 'crop']
['farming', 'activity', 'brings', 'income']
['soil', 'get', 'degraded', 'movebto', 'another', 'portion', 'land']
['source', 'income']
['people', 'burn', 'agriculture', 'area', 'looking', 'rat', 'addition', 'burn', 'food', 'crop', 'due', 'search', 'rat']
['cutting', 'tree', 'destroy', 'change', 'landscape', 'livelihood']
['tree', 'challenge', 'growung', 'burned']
['people', 'cutting', 'tree', 'charcoal', 'destroy', 'difficult', 'regeneration']
['Source', 'income']
['tree', 'dry', 'burnt']
['fire', 'cause', 'lot', 'damage', 'biodiversity', 'well', 'plant']
['tree', 'get', 'burn', 'reducing', 'regeneration', 'well', 'fertility', 'soil']
['tree', 'get', 'destroyed', 'burnt', 'difficult', 'regenerate']
['cutting', 'tree', 'charcoal', 'prevents', 'regeneration']
['cutting', 'tree', 'charcoal', 'requires', 'huge', 'land', 'compared', 'farm', 'one', 'partition', 'land']
['small', 'biodiversity', 'destroyed', 'fire']
['Burning', 'destroys', 'tree']
['fire', 'burn', 'tree', 'lead', 'dry']
['people', 'cutting', 'huge', 'chuck', 'land', 'cultivation', 'crop']
['burning', 'lot', 'thing', 'like', 'snake', 'house', 'important', 'biodiversity', 'destroyed']
['source', 'livelihood']
['Thats', 'source', 'food', 'crop']
['people', 'cutting', 'tree', 'anyhow']
['cutting', 'tree', 'charcoal', 'finish', 'tree', 'others']
['burning', 'destroys', 'fertility', 'soil']
['reason', 'told', 'start', 'conservation', 'farming', 'entail', 'farming', 'locality']
['Chitemene', 'system', 'burning', 'disallowed']
['main', 'purpose', 'livelihood']
['fire', 'destroys', 'lot', 'thing', 'air', 'breathe', 'soil', 'fertility', 'small', 'animal', 'plant']
['land', 'cultivated', 'done', 'big', 'land', 'transforms', 'landscape']
['fire', 'destroys', 'flower', 'production', 'honey', 'reduced']
['people', 'make', 'charcoal', 'cut', 'tree', 'fresh', 'cut', 'huge', 'area']
['destroys', 'lot', 'biodiversity', 'egg', 'bird', 'snake']
['livelihood', 'depends', 'activity', 'charcoal', 'burning']
['animal', 'always', 'grazing', 'vegetation', 'room', 'given', 'plant', 'sprout']
['tree', 'cut', 'charcoal', 'stem', 'dy', 'replacement']
['Fire', 'destroys', 'everything', 'others', 'even', 'biodiversity', 'get', 'killed']
['fire', 'cut', 'across', 'huge', 'area', 'kill', 'everything', 'way']
['land', 'used', 'agriculture', 'purpose', 'cutting', 'done', 'large', 'scale']
['Burning', 'destroys', 'life', 'everything']
['fire', 'destroys', 'almost', 'everything']
['cause', 'soil', 'erossion']
['agriculture', 'activity', 'uprooting', 'tree', 'thus', 'distruction', 'environment']
['burning', 'cover', 'huge', 'area', 'kill', 'biodiversity', 'way']
['making', 'charcoal', 'mainly', 'focus', 'big', 'tree', 'make', 'desert', 'area']
['destroys', 'tree', 'everyone', 'cutting', 'tree', 'energy']
['tree', 'cut', 'charcoal', 'take', 'time', 'grow', 'thus', 'causing', 'climate', 'change', 'turn', 'affecting', 'main', 'livelihood', 'agriculture']
['cut', 'tree', 'rainfall', 'reduce', 'lead', 'animal', 'dying', 'thirst']
['clear', 'law', 'prevents', 'people', 'scared', 'burning', 'bush']
['huge', 'land', 'cleared', 'garden']
['continuous', 'process', 'cutting', 'tree', 'charcoal', 'thus', 'destroys', 'landscape']
['rainfall', 'reduce', 'would', 'much', 'wind', 'well', 'climate', 'change']
['tree', 'reduced', 'going', 'problem', 'rainfall']
['lot', 'famers', 'thus', 'major', 'contributor']
['Source', 'livelihood', 'crop']
['farmer']
['burn', 'bush', 'destroys', 'tree', 'well', 'animal']
['burning', 'make', 'tree', 'dry', 'well', 'young', 'animal', 'get', 'killed']
['fire', 'distroys', 'almost', 'everything', 'even', 'bird', 'forest', 'destroyed']
['tradition', 'cultivation', 'survive', 'tradition', 'burning', 'destroys', 'lot', 'thing', 'hence', 'major', 'contributor']
['burning', 'bush', 'destroys', 'environment']
['animal', 'depend', 'grass', 'thus', 'burnt', 'animal', 'would', 'die']
['depend', 'farming', 'livelihood']
['charcoal', 'people', 'burn', 'big', 'tree', 'destroys', 'habitat', 'animal']
['Anyone', 'burn', 'bush', 'would', 'burn', 'forest', 'fire', 'guard', 'prevent', 'forest', 'burnt']
['knowledge', 'protect', 'environment', 'people', 'thing', 'without', 'knowledge', 'protect', 'environment', 'thus', 'need', 'protect']
['grass', 'grazing', 'animal', 'would', 'destroyed']
['destroys', 'food', 'elephant', 'depends', 'burnt']
['Fire', 'destroys', 'habitat', 'animal']
['animal', 'feed', 'grass', 'grass', 'burnt', 'animal', 'come', 'community', 'disturb']
['burn', 'destroys', 'habitat', 'animal']
['Cutting', 'tree', 'come', 'strong', 'wind']
['Thats', 'food', 'come']
['Cutting', 'tree', 'make', 'gardenmatema', 'meaning', 'tree', 'replaced']
['Fire', 'burn', 'everything', 'term', 'life']
['fire', 'destroys', 'grazing', 'grass', 'animal']
['People', 'always', 'burning', 'bush', 'without', 'control']
['destroys', 'life']
['Fire', 'burn', 'whole', 'area']
['Burning', 'kill', 'lot', 'thing', 'biodiversity']
['tree', 'cut', 'lot', 'thus', 'distructs', 'lot']
['tree', 'cut', 'grow']
['cutting', 'tree', 'brings', 'drought']
['tree', 'cut', 'area', 'possing', 'treat', 'strong', 'wind']
['rain', 'water', 'flow', 'due', 'lack', 'tree', 'block']
['lead', 'climate', 'due', 'lack', 'tree']
['animal', 'graz', 'thus', 'make', 'move', 'long', 'distance', 'find', 'pasture']
['rainfall', 'back', 'green', 'vegetation', 'thus', 'burnt', 'less', 'rainfall']
['tree', 'get', 'burnt', 'dry', 'thats', 'reason', 'less', 'rainfall']
['use', 'Chitemene', 'system', 'cutting', 'tree', 'charcoal']
['tree', 'cut', 'made', 'charcoal', 'trunk', 'stem', 'grow']
['destroys', 'everything', 'way']
['destroys', 'everything', 'path']

CLT_R1["Contributor_Landscape_Transformation_Reasons"] = CLT_R1["Contributor_Landscape_Transformation_Reasons"].fillna("")  
CLT_R1["Contributor_Landscape_Transformation_Reasons"] = CLT_R1["Contributor_Landscape_Transformation_Reasons"].astype(str)
CLT_R1_Text = " ".join(CLT_R1["Contributor_Landscape_Transformation_Reasons"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(CLT_R1_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Contributor Landscape Transformation", loc="left", fontsize=20, pad=20)
plt.axis("off")  
plt.show()

8.5 The Landscape Depended on for a Livelihood

LDL_grouped = df2.groupby('Name_Main_Project')['Landscape_Depended_Livelihood'].value_counts(dropna=False)

LDL_grouped

Name_Main_Project                     Landscape_Depended_Livelihood                 
EbA_CENTRAL_MUCHINGA_LUAPULA          Agriculture_Areas_Dependent                        3
Ecosystem Conservation_NORTH_WESTERN  NaN                                                5
                                      Agriculture_Areas_Dependent                        3
                                      Forest_Dependent                                   2
                                      Wetlands_Dependent                                 1
PIN_WESTERN                           Agriculture_Areas_Dependent                        5
                                      NaN                                                1
SCRALA_SOUTHERN_WESTERN_NORTHEN       Agriculture_Areas_Dependent                       21
                                      Wetlands_Dependent                                 6
                                      Forest_Dependent                                   2
                                      NaN                                                1
SCReBS_WESTERN                        Agriculture_Areas_Dependent                       10
                                      Wetlands_Dependent                                 5
SCRiKA_LS                             Agriculture_Areas_Dependent                       30
                                      Forest_Dependent                                   4
                                      Wetlands_Dependent                                 4
                                      NaN                                                4
                                      Wetlands_Dependent,Forest_Dependent                2
TRALARD_LNM                           Agriculture_Areas_Dependent                       20
                                      Wetlands_Dependent                                 9
                                      Forest_Dependent                                   5
                                      Wetlands_Dependent,Forest_Dependent                2
                                      Agriculture_Areas_Dependent,Wetlands_Dependent     1
                                      Forest_Dependent,Wetlands_Dependent                1
                                      Wetlands_Dependent,Agriculture_Areas_Dependent     1
                                      NaN                                                1
Name: count, dtype: int64

LDL_grouped1 = pd.DataFrame(LDL_grouped)

LDL_grouped1

		count
Name_Main_Project	Landscape_Depended_Livelihood
EbA_CENTRAL_MUCHINGA_LUAPULA	Agriculture_Areas_Dependent	3
Ecosystem Conservation_NORTH_WESTERN	NaN	5
	Agriculture_Areas_Dependent	3
	Forest_Dependent	2
	Wetlands_Dependent	1
PIN_WESTERN	Agriculture_Areas_Dependent	5
	NaN	1
SCRALA_SOUTHERN_WESTERN_NORTHEN	Agriculture_Areas_Dependent	21
	Wetlands_Dependent	6
	Forest_Dependent	2
	NaN	1
SCReBS_WESTERN	Agriculture_Areas_Dependent	10
	Wetlands_Dependent	5
SCRiKA_LS	Agriculture_Areas_Dependent	30
	Forest_Dependent	4
	Wetlands_Dependent	4
	NaN	4
	Wetlands_Dependent,Forest_Dependent	2
TRALARD_LNM	Agriculture_Areas_Dependent	20
	Wetlands_Dependent	9
	Forest_Dependent	5
	Wetlands_Dependent,Forest_Dependent	2
	Agriculture_Areas_Dependent,Wetlands_Dependent	1
	Forest_Dependent,Wetlands_Dependent	1
	Wetlands_Dependent,Agriculture_Areas_Dependent	1
	NaN	1

plt.figure(figsize=(8.7, 8.27))
hue_order = ["Agriculture_Areas_Dependent", "Wetlands_Dependent", "Forest_Dependent", "NaN"]
ax = sns.barplot(data = LDL_grouped1, x="count", y="Name_Main_Project", hue="Landscape_Depended_Livelihood", hue_order=hue_order, legend=True)
ax.set_title("Figure 7a: Landscape Dependent on by Livelihoods", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()

LDL = df4['Landscape_Depended_Livelihood'].value_counts(dropna=False)
LDL_P = (df4['Landscape_Depended_Livelihood'].value_counts(normalize=True, dropna=False).round(2))

LDL_P

Landscape_Depended_Livelihood
Agriculture_Areas_Dependent                       0.61
Wetlands_Dependent                                0.17
Forest_Dependent                                  0.09
NaN                                               0.09
Wetlands_Dependent,Forest_Dependent               0.03
Agriculture_Areas_Dependent,Wetlands_Dependent    0.01
Forest_Dependent,Wetlands_Dependent               0.01
Wetlands_Dependent,Agriculture_Areas_Dependent    0.01
Name: proportion, dtype: float64

df_LDL = pd.DataFrame(LDL)

df_LDL

	count
Landscape_Depended_Livelihood
Agriculture_Areas_Dependent	92
Wetlands_Dependent	25
Forest_Dependent	13
NaN	13
Wetlands_Dependent,Forest_Dependent	4
Agriculture_Areas_Dependent,Wetlands_Dependent	1
Forest_Dependent,Wetlands_Dependent	1
Wetlands_Dependent,Agriculture_Areas_Dependent	1

ax = sns.countplot(df4["Landscape_Depended_Livelihood"])
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
    ax.set_title("Figure 7b: Landscape Dependent on by Livelihoods", fontsize=14)
plt.show()

agreement_levels = ["Agriculture_Areas_Dependent", "Wetlands_Dependent","Forest_Dependent"]
LDL_R = df2[df2["Landscape_Depended_Livelihood"].isin(agreement_levels)]
LDL_R1 = LDL_R.drop(LDL_R.columns[[0,1,2,3,4,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,31,32,33,34]], axis = 1)
LDL_R1grouped = LDL_R1.groupby('Name_Main_Project')['Landscape_Depended_Livelihood']
#LDL_R1

lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in LDL_R1.iterrows():
    LDL_R1_filter_sentence = []
    LDL_R1_sentence = row["Landscape_Depeneded_Livelihood_Reasons"]
    if pd.isnull(LDL_R1_sentence):
       continue
    LDL_R1_sentence_cleaned = re.sub(r'[^\w\s]','',LDL_R1_sentence)
    LDL_R1_words = nltk.word_tokenize(LDL_R1_sentence_cleaned)
    LDL_R1_words = [lemmatizer.lemmatize(w) for w in LDL_R1_words if w.lower() not in stop_words]
    LDL_R1_filter_sentence.extend(LDL_R1_words)
    print(LDL_R1_filter_sentence)

['cultivates', 'large', 'area', 'land', 'crop', 'sold', 'livelihood']
['catching', 'fish', 'source', 'income', 'area']
['Food', 'main', 'source', 'livelihood', 'thus', 'without', 'way', 'earn', 'living']
['source', 'income', 'well', 'food', 'consumption']
['farming', 'help', 'sourcing', 'food']
['Thats', 'source', 'income']
['source', 'livelihood']
['income', 'come', 'living']
['help', 'u', 'cultivate', 'cassava', 'maize', 'much', 'fishing', 'activity']
['farming', 'obtain', 'food', 'consumption', 'household', 'level']
['food', 'security', 'come']
['tree', 'bring', 'rainfall', 'area', 'compared', 'area']
['source', 'income']
['majority', 'people', 'cultivate', 'land', 'livelihood', 'depending', 'buying', 'shop']
['less', 'rainfall', 'help', 'u', 'source', 'water']
['Thats', 'whats', 'common', 'within', 'area']
['source', 'traditional', 'medicine']
['provides', 'water', 'drinking', 'water', 'life']
['Water', 'source', 'life']
['source', 'food', 'crop', 'income']
['use', 'cultivation']
['Source', 'income']
['mostly', 'farmer']
['Thats', 'source', 'income', 'livelihood']
['lot', 'activity', 'come', 'water']
['food', 'crop']
['food', 'crop', 'come']
['Thats', 'source', 'food', 'crop', 'well', 'income']
['Thats', 'grow', 'crop', 'livelihood']
['livelihood', 'farming', 'main', 'stay']
['provides', 'resource', 'people', 'term', 'wood']
['obtain', 'food', 'crop', 'assist', 'u', 'livelihood']
['Water', 'life', 'used', 'water', 'environment', 'tree', 'grow', 'prevent', 'developing', 'desert']
['source', 'crop', 'food']
['Thats', 'grow', 'crop', 'livelihood']
['Source', 'life']
['portion', 'land', 'cultivating', 'one', 'area', 'thus', 'cutting', 'tree']
['Thats', 'get', 'food', 'product', 'livelihood']
['Water', 'life']
['Water', 'life']
['Water', 'life']
['answer']
['crop', 'income', 'come']
['Thats', 'get', 'food', 'eat']
['Source', 'livelihood']
['Life', 'water']
['Water', 'life', 'evryone', 'drink']
['income', 'food', 'crop', 'come']
['tree', 'rain', 'much', 'well', 'soil', 'fertile']
['Water', 'life']
['Thats', 'get', 'food', 'crop', 'survival']
['every', 'individual', 'depend', 'agriculture', 'land']
['Water', 'life', 'thus', 'water', 'plant', 'dry']
['source', 'mushroom', 'catapilars', 'forest', 'protected', 'well', 'working', 'honarary', 'officer']
['Water', 'life', 'used', 'every', 'situation', 'cultivation', 'watering', 'garden']
['brings', 'u', 'food', 'well', 'income']
['flood', 'cattle', 'go', 'forest', 'area', 'graz']
['Thats', 'get', 'food']
['farmer']
['farm', 'product']
['Thats', 'get', 'food', 'crop']
['Thats', 'get', 'food', 'crop']
['get', 'food', 'crop', 'livelihood', 'dependent']
['livelihood', 'based', 'farming']
['source', 'source', 'livelihood']
['get', 'food', 'crop', 'livehoods']
['area', 'farming', 'found', 'forest', 'area']
['cattle', 'graze']
['cultivate', 'source', 'food', 'crop', 'game', 'park', 'help', 'depend', 'ZAWA', 'Officers', 'give', 'resource']
['farmer', 'nature']
['Thats', 'source', 'food', 'crop']
['Thats', 'food', 'come']
['Thats', 'get', 'food', 'eating', 'livelihood']
['cultivate', 'get', 'crop']
['food', 'income', 'come', 'help', 'u']
['Thats', 'get', 'maize', 'staple', 'food', 'farming']
['keeping', 'bird', 'gardening', 'thus', 'forest', 'protected', 'well', 'animal']
['found']
['Source', 'food', 'crop']
['wetland', 'dry', 'thus', 'depend', 'agriculture', 'food', 'crop']
['Thats', 'get', 'crop', 'food']
['food', 'gotten', 'borehole', 'sank']
['farmer']
['Thats', 'animal', 'feed']
['Thats', 'get', 'income']
['Everything', 'come', 'agriculture']
['farm', 'crop']
['Water', 'source', 'life', 'animal', 'cattle']
['farmer', 'nature']
['farmer', 'nature', 'southern', 'province']
['Thats', 'get', 'income']
['Water', 'life', 'cattle', 'drink', 'water', 'wetland']
['Water', 'life', 'domesticated', 'animal', 'need', 'water', 'depend', 'animal']
['Thats', 'get', 'food', 'crop']
['Thats', 'get', 'food', 'crop', 'income']
['agriculture', 'get', 'food', 'crop', 'income']
['major', 'activity', 'around', 'area']
['food', 'crop']
['Thats', 'get', 'food', 'living']
['Thats', 'get', 'food', 'crop']
['farmer']
['Thats', 'get', 'food']
['farming']
['farmer']
['Thats', 'food', 'come']
['farmer']
['Thats', 'animal', 'graze']
['flood', 'go', 'leave', 'moisture', 'thats', 'help', 'people', 'grow', 'crop']
['Thats', 'grow', 'food', 'crop']
['wetland', 'cultivate', 'rice', 'well', 'get', 'water', 'watering', 'garden']
['get', 'food', 'eating']
['Thats', 'grow', 'crop']
['Thats', 'get', 'food']
['Thats', 'food', 'crop', 'come']
['Thats', 'food', 'crop', 'come']
['Thats', 'get', 'food', 'crop']
['Thats', 'get', 'food', 'crop']
['farmer', 'rice']
['get', 'food', 'crop']
['cultivate', 'flood', 'plain', 'animal', 'graze']
['Thats', 'get', 'food']
['farmer']
['get', 'crop']
['farmer']
['Thats', 'get', 'food', 'money']
['food', 'crop', 'found']
['Thats', 'plant', 'maize', 'rice']

LDL_R1["Landscape_Depeneded_Livelihood_Reasons"] = LDL_R1["Landscape_Depeneded_Livelihood_Reasons"].fillna("")  
LDL_R1["Landscape_Depeneded_Livelihood_Reasons"] = LDL_R1["Landscape_Depeneded_Livelihood_Reasons"].astype(str)
LDL_R1_Text = " ".join(LDL_R1["Landscape_Depeneded_Livelihood_Reasons"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(LDL_R1_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Landscape Depeneded Livelihood", loc="left", fontsize=20, pad=20)
plt.axis("off")  
plt.show()

8.6 Explanation to Choices

It gives an explanation to the choice selected that is not among the choices that were provided in the list of the questionnaire

df5=df2.drop(df2.columns[[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]], axis = 1)

#df5

lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in df5.iterrows():
    PL_RS1_filter_sentence = []
    PL_RS1_sentence = row["Purpose_Landscape_Specific"]
    if pd.isnull(PL_RS1_sentence):
       continue
    PL_RS1_sentence_cleaned = re.sub(r'[^\w\s]','',PL_RS1_sentence)
    PL_RS1_words = nltk.word_tokenize(PL_RS1_sentence_cleaned)
    PL_RS1_words = [lemmatizer.lemmatize(w) for w in PL_RS1_words if w.lower() not in stop_words]
    PL_RS1_filter_sentence.extend(PL_RS1_words)
    print(PL_RS1_filter_sentence)

['protection', 'nature', 'others', 'important']
['fire', 'burn', 'biodiversity', 'allow', 'environmental', 'process']
['tourist', 'bring', 'income', 'term', 'viewing', 'animal']
['source', 'income', 'tourism']

lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in df5.iterrows():
    CN_RS1_filter_sentence = []
    CN_RS1_sentence = row["Connection_Nature_Specific"]
    if pd.isnull(CN_RS1_sentence):
       continue
    CN_RS1_sentence_cleaned = re.sub(r'[^\w\s]','',CN_RS1_sentence)
    CN_RS1_words = nltk.word_tokenize(CN_RS1_sentence_cleaned)
    CN_RS1_words = [lemmatizer.lemmatize(w) for w in CN_RS1_words if w.lower() not in stop_words]
    CN_RS1_filter_sentence.extend(CN_RS1_words)
    print(CN_RS1_filter_sentence)

['Taking', 'care', 'nature', 'without', 'destroying']
['Taking', 'care', 'nature']
['Source', 'good', 'air', 'food', 'product']
['related']
['Protection', 'tree']
['Taking', 'care', 'animal']

lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in df5.iterrows():
    CLT_RS1_filter_sentence = []
    CLT_RS1_sentence = row["Contributor_Landscape_Transformation_Specific"]
    if pd.isnull(CLT_RS1_sentence):
       continue
    CLT_RS1_sentence_cleaned = re.sub(r'[^\w\s]','',CLT_RS1_sentence)
    CLT_RS1_words = nltk.word_tokenize(CLT_RS1_sentence_cleaned)
    CLT_RS1_words = [lemmatizer.lemmatize(w) for w in CLT_RS1_words if w.lower() not in stop_words]
    CLT_RS1_filter_sentence.extend(CLT_RS1_words)
    print(CLT_RS1_filter_sentence)

['Cutting', 'tree', 'sale']

lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in df5.iterrows():
    LDL_RS1_filter_sentence = []
    LDL_RS1_sentence = row["Landscape_Depended_Livelihood_Specific"]
    if pd.isnull(LDL_RS1_sentence):
       continue
    LDL_RS1_sentence_cleaned = re.sub(r'[^\w\s]','',LDL_RS1_sentence)
    LDL_RS1_words = nltk.word_tokenize(LDL_RS1_sentence_cleaned)
    LDL_RS1_words = [lemmatizer.lemmatize(w) for w in LDL_RS1_words if w.lower() not in stop_words]
    LDL_RS1_filter_sentence.extend(LDL_RS1_words)
    print(LDL_RS1_filter_sentence)

9. Description Statistics

It gives a background to the study sites

df6=df2.drop(df2.columns[[0,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]], axis = 1)

#df6

[Back to top]

9.1 Main project Types

This shows the number of questionaires that were administered to each project type

MP = df6['Name_Main_Project'].value_counts(dropna=False)

MP

Name_Main_Project
SCRiKA_LS                               44
TRALARD_LNM                             40
SCRALA_SOUTHERN_WESTERN_NORTHEN         30
SCReBS_WESTERN                          15
Ecosystem Conservation_NORTH_WESTERN    11
PIN_WESTERN                              6
EbA_CENTRAL_MUCHINGA_LUAPULA             3
NaN                                      1
Name: count, dtype: int64

ax = sns.countplot(df6["Name_Main_Project"])
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
    ax.set_title("Figure 8: The Total Number of Respondents in each of the Main Project", fontsize=14)
plt.show()

9.2 The Wards

The wards that had respodents and their number

WN = df6['Ward_Name'].value_counts(dropna=False)

WN

Ward_Name
Namwala Central ward    17
Omba ward               15
Mbila ward              14
Yeta ward               11
Mwanambuyu ward         11
Lulimala ward           11
Isamba ward             10
Kalobolelwa ward         9
Ntonga ward              8
Kalanga ward             8
Moofwe ward              7
Chitimbwa ward           7
Makuya ward              6
Nachikufu ward           5
Luubwe ward              4
Ntambu ward              4
NaN                      2
Silunga ward             1
Name: count, dtype: int64

ax = sns.countplot(df6["Ward_Name"])
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
    ax.set_title("Figure 9: Number of Respondents in each Ward", fontsize=14)
plt.show()

9.3 Number of beneficiaries

It shows the number of households that benefited from the interviewees

NB = df6['Number_Beneficiaries'].value_counts(dropna=False)

NB

Number_Beneficiaries
Greaterthan40People_Many    40
10to20People_Many           25
NaN                         25
20to30People_Many           24
30to40People_Many           23
Lessthan10People_Many       13
Name: count, dtype: int64

ax = sns.countplot(df6["Number_Beneficiaries"])
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
    ax.set_title("Figure 10: Number of household beneficiaries in each Cohort Category", fontsize=14)
plt.show()

9.4 Size of Landscape

SIZE = df6['Size'].value_counts(dropna=False)

SIZE

Size
Lessthan4Lima_Size        48
Greaterthan16Lima_Size    40
NotApplicable_Size        31
4to8Lima_Size             13
NaN                       10
8to12Lima_Size             5
12to16Lima_Size            3
Name: count, dtype: int64

ax = sns.countplot(df6["Size"])
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
    ax.set_title("Figure 11: Number of beneficiaries in each Land Size Category", fontsize=14)
plt.show()

9.5 Represing Others

It looks at those representing one house and more

RO = df6['Representing_Others'].value_counts(dropna=False)

RO

Representing_Others
yes    125
no      23
NaN      2
Name: count, dtype: int64

ax = sns.countplot(df6["Representing_Others"])
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
    ax.set_title("Figure 12: Number of beneficiaries Representing Others", fontsize=14)
plt.show()

[Back to top]

10. Reasons

The pandas dataframe depicts columns for the reasons to the responses to the likert scales

df7=df2.drop(df2.columns[[0,1,2,3,4,5,6,7,8,10,11,13,14,15,17,18,19,20,22,23,24,25,26,27,29,31,32,33,34]], axis = 1)

#df7

10.1 Reasons for Cultural Practices Hidering

 The reasons for cultural practices that hinder transfromation

lemmatizer = WordNetLemmatizer()
CPH = df7['Cultural_Practices_Hinder_Reason'].str.lower().str.cat(sep=' ')
CPH_words = nltk.tokenize.word_tokenize(CPH)
CPH_filtered_tokens = [word for word in CPH_words if len(CPH_words) >= 8]
CPH_lemmatized_words = [lemmatizer.lemmatize(word) for word in CPH_filtered_tokens]
CPH_token_counts = Counter(CPH_lemmatized_words)
CPH_columns = pd.DataFrame(CPH_token_counts.most_common(67),
columns = ['Word', 'Frequency'])
#print(CPH_columns)

bigrams_list = list(bigrams(CPH_filtered_tokens))
#print(bigrams_list)
CPH_bigram_counts = Counter(zip(bigrams_list, bigrams_list[1:]))
#print(bigram_counts)  
CPH_bigrams = pd.DataFrame(CPH_bigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CPH_bigrams)

                                           Word  Frequency
0                  ((there, is), (is, nothing))         73
1             ((is, nothing), (nothing, there))         55
2               ((nothing, there), (there, is))         52
3          ((protect, the), (the, environment))          6
4               ((to, protect), (protect, the))          6
5                       ((there, is), (is, no))          5
6                     ((there, are), (are, no))          4
7    ((the, environment), (environment, there))          4
8               ((is, nothing), (nothing, the))          4
9   ((customary, practices), (practices, that))          3
10                     ((as, well), (well, as))          3
11                  ((are, no), (no, cultural))          3
12             ((nothing, there), (there, are))          3
13        ((destroys, the), (the, environment))          3
14                ((cutting, down), (down, of))          3
15          ((environment, there), (there, is))          3
16                ((is, nothing), (nothing, i))          3
17                    ((``, ''), ('', malende))          3
18               (('', malende), (malende, ''))          3
19                    ((malende, ''), ('', ''))          3
20                   ((is, no), (no, cultural))          2
21                 ((there, are), (are, rules))          2
22                       ((there, is), (is, a))          2
23                   ((that, the), (the, bush))          2
24                        ((it, is), (is, not))          2
25   ((are, customary), (customary, practices))          2
26      ((a, customary), (customary, practice))          2
27          ((that, destroys), (destroys, the))          2
28                    ((down, of), (of, trees))          2
29                 ((along, the), (the, river))          2
30                    ((a, long), (long, time))          2
31                       ((but, at), (at, the))          2
32                   ((at, the), (the, moment))          2
33      ((no, cultural), (cultural, practices))          2
34       ((the, natural), (natural, resources))          2
35                ((there, are), (are, places))          2
36                    ((nothing, i), (i, have))          2
37              ((is, nothing), (nothing, our))          2
38                    ((us, to), (to, protect))          2
39              ((of, the), (the, environment))          2
40     ((the, environment), (environment, the))          2
41                       ((on, how), (how, to))          2
42                   ((how, to), (to, protect))          2
43                ((trees, there), (there, is))          2
44                 ((cut, down), (down, trees))          2
45                     ((called, ``), (``, ''))          2
46                       (('', ''), ('', that))          2
47                   ((trees, but), (but, the))          2
48                    ((of, the), (the, trees))          2
49                    ((it, has), (has, never))          1
50              ((has, never), (never, happed))          1
51          ((never, happed), (happed, before))          1
52             ((happed, before), (before, in))          1
53                    ((before, in), (in, his))          1
54                     ((in, his), (his, life))          1
55                  ((his, life), (life, time))          1
56                ((life, time), (time, there))          1
57                 ((time, there), (there, is))          1
58                    ((there, is), (is, need))          1
59                     ((is, need), (need, to))          1
60                  ((need, to), (to, harvest))          1
61            ((to, harvest), (harvest, trees))          1
62            ((harvest, trees), (trees, when))          1
63                ((trees, when), (when, they))          1
64                 ((when, they), (they, have))          1
65                ((they, have), (have, fully))          1
66              ((have, fully), (fully, grown))          1

trigrams_list = list(trigrams(CPH_filtered_tokens))
#print(bigrams_list)
CPH_trigram_counts = Counter(zip(trigrams_list, trigrams_list[1:]))
#print(bigram_counts)  
CPH_trigrams = pd.DataFrame(CPH_trigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CPH_trigrams)

                                                                     Word  \
0                            ((there, is, nothing), (is, nothing, there))   
1                            ((is, nothing, there), (nothing, there, is))   
2                            ((nothing, there, is), (there, is, nothing))   
3                       ((to, protect, the), (protect, the, environment))   
4                              ((there, is, nothing), (is, nothing, the))   
5                                 ((there, are, no), (are, no, cultural))   
6                           ((is, nothing, there), (nothing, there, are))   
7                   ((the, environment, there), (environment, there, is))   
8                        ((environment, there, is), (there, is, nothing))   
9                                ((there, is, nothing), (is, nothing, i))   
10                                 ((``, '', malende), ('', malende, ''))   
11                                 (('', malende, ''), (malende, '', ''))   
12                                ((nothing, there, is), (there, is, no))   
13                                  ((there, is, no), (is, no, cultural))   
14            ((are, customary, practices), (customary, practices, that))   
15                  ((that, destroys, the), (destroys, the, environment))   
16              ((destroys, the, environment), (the, environment, there))   
17                               ((cutting, down, of), (down, of, trees))   
18                                    ((but, at, the), (at, the, moment))   
19                       ((are, no, cultural), (no, cultural, practices))   
20                                 ((is, nothing, i), (nothing, i, have))   
21                             ((there, is, nothing), (is, nothing, our))   
22                                ((us, to, protect), (to, protect, the))   
23                                    ((on, how, to), (how, to, protect))   
24                               ((how, to, protect), (to, protect, the))   
25                               ((it, has, never), (has, never, happed))   
26                        ((has, never, happed), (never, happed, before))   
27                        ((never, happed, before), (happed, before, in))   
28                              ((happed, before, in), (before, in, his))   
29                                   ((before, in, his), (in, his, life))   
30                                   ((in, his, life), (his, life, time))   
31                               ((his, life, time), (life, time, there))   
32                               ((life, time, there), (time, there, is))   
33                                 ((time, there, is), (there, is, need))   
34                                    ((there, is, need), (is, need, to))   
35                                  ((is, need, to), (need, to, harvest))   
36                            ((need, to, harvest), (to, harvest, trees))   
37                         ((to, harvest, trees), (harvest, trees, when))   
38                          ((harvest, trees, when), (trees, when, they))   
39                              ((trees, when, they), (when, they, have))   
40                              ((when, they, have), (they, have, fully))   
41                            ((they, have, fully), (have, fully, grown))   
42                            ((have, fully, grown), (fully, grown, the))   
43                              ((fully, grown, the), (grown, the, same))   
44                             ((grown, the, same), (the, same, applies))   
45                            ((the, same, applies), (same, applies, to))   
46                     ((same, applies, to), (applies, to, biodiversity))   
47               ((applies, to, biodiversity), (to, biodiversity, there))   
48                ((to, biodiversity, there), (biodiversity, there, are))   
49                         ((biodiversity, there, are), (there, are, no))   
50                               ((there, are, no), (are, no, customary))   
51                     ((are, no, customary), (no, customary, practices))   
52             ((no, customary, practices), (customary, practices, that))   
53              ((customary, practices, that), (practices, that, hinder))   
54               ((practices, that, hinder), (that, hinder, sustainable))   
55       ((that, hinder, sustainable), (hinder, sustainable, management))   
56  ((hinder, sustainable, management), (sustainable, management, there))   
57           ((sustainable, management, there), (management, there, are))   
58                         ((management, there, are), (there, are, crop))   
59                           ((there, are, crop), (are, crop, rotations))   
60                      ((are, crop, rotations), (crop, rotations, made))   
61                       ((crop, rotations, made), (rotations, made, on))   
62                               ((rotations, made, on), (made, on, the))   
63                                     ((made, on, the), (on, the, land))   
64                                   ((on, the, land), (the, land, when))   
65                         ((the, land, when), (land, when, cultivating))   
66                   ((land, when, cultivating), (when, cultivating, to))   

    Frequency  
0          54  
1          52  
2          47  
3           5  
4           4  
5           3  
6           3  
7           3  
8           3  
9           3  
10          3  
11          3  
12          2  
13          2  
14          2  
15          2  
16          2  
17          2  
18          2  
19          2  
20          2  
21          2  
22          2  
23          2  
24          2  
25          1  
26          1  
27          1  
28          1  
29          1  
30          1  
31          1  
32          1  
33          1  
34          1  
35          1  
36          1  
37          1  
38          1  
39          1  
40          1  
41          1  
42          1  
43          1  
44          1  
45          1  
46          1  
47          1  
48          1  
49          1  
50          1  
51          1  
52          1  
53          1  
54          1  
55          1  
56          1  
57          1  
58          1  
59          1  
60          1  
61          1  
62          1  
63          1  
64          1  
65          1  
66          1  

lemmatizer = WordNetLemmatizer()
CAC = df7['Cultural_Aspects_Considered_Reasons'].str.lower().str.cat(sep=' ')
CAC_words = nltk.tokenize.word_tokenize(CAC)
CAC_filtered_tokens = [word for word in CAC_words if len(CAC_words) >= 4]
CAC_lemmatized_words = [lemmatizer.lemmatize(word) for word in CAC_filtered_tokens]
CAC_token_counts = Counter(CAC_lemmatized_words)
CAC_columns = pd.DataFrame(CAC_token_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CAC_columns)

           Word  Frequency
0           the         73
1           not         52
2            is         51
3            we         47
4         there         45
5       nothing         39
6            do         36
7            to         23
8           are         23
9            it         17
10      project         15
11         that         13
12           of         13
13     cultural         12
14         have         11
15          can         11
16     included         11
17         they         11
18           no         10
19           in         10
20            a         10
21      because         10
22  traditional          8
23       aspect          7
24       follow          7
25     practice          7
26          and          6
27           be          6
28           if          6
29          our          6
30          for          5
31         from          5
32      include          5
33   considered          5
34         what          4
35           so          4
36        chief          4
37         give          4
38           or          4
39       answer          4
40    authority          4
41           at          3
42         time          3
43         land          3
44         only          3
45   government          3
46            u          3
47         when          3
48          law          3
49        place          3
50        would          3
51         well          3
52    customary          3
53    destroyed          3
54         done          3
55          but          3
56       people          3
57       change          3
58    community          3
59      history          2
60         need          2
61         come          2
62         with          2
63          say          2
64            ,          2
65         tree          2
66          cut          2

CAC_bigrams_list = list(bigrams(CAC_filtered_tokens))
#print(bigrams_list)
CAC_bigram_counts = Counter(zip(CAC_bigrams_list, CAC_bigrams_list[1:]))
#print(bigram_counts)  
CAC_bigrams = pd.DataFrame(CAC_bigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CAC_bigrams)

                                              Word  Frequency
0                     ((there, is), (is, nothing))         33
1                            ((we, do), (do, not))         26
2                   ((is, nothing), (nothing, we))          9
3                        ((nothing, we), (we, do))          9
4                ((is, nothing), (nothing, there))          8
5                  ((nothing, there), (there, is))          7
6                           ((do, not), (not, do))          7
7                      ((do, not), (not, include))          5
8                        ((they, are), (are, not))          5
9                            ((not, do), (do, it))          5
10          ((the, cultural), (cultural, aspects))          5
11                   ((are, not), (not, included))          4
12                       ((there, are), (are, no))          3
13                 ((is, nothing), (nothing, the))          3
14  ((the, traditional), (traditional, authority))          3
15           ((cultural, aspects), (aspects, are))          3
16               ((not, included), (included, we))          3
17                      ((included, we), (we, do))          3
18                    ((that, there), (there, is))          2
19                    ((we, only), (only, follow))          2
20                        ((we, have), (have, to))          2
21                           ((has, to), (to, be))          2
22                  ((is, nothing), (nothing, at))          2
23                      ((nothing, at), (at, the))          2
24                      ((at, the), (the, moment))          2
25                   ((we, follow), (follow, the))          2
26                    ((follow, the), (the, laws))          2
27                ((the, project), (project, can))          2
28                     ((if, there), (there, are))          2
29                    ((are, no), (no, customary))          2
30       ((no, customary), (customary, practices))          2
31                            ((if, it), (it, is))          2
32                           ((it, is), (is, not))          2
33                     ((not, work), (work, well))          2
34                         ((not, do), (do, that))          2
35                  ((is, nothing), (nothing, no))          2
36                   ((nothing, no), (no, answer))          2
37                            ((do, it), (it, we))          2
38                        ((do, not), (not, have))          2
39                ((we, always), (always, follow))          2
40              ((not, include), (include, there))          2
41                 ((include, there), (there, is))          2
42                ((is, nothing), (nothing, they))          2
43                         ((can, not), (not, be))          2
44                         ((do, not), (not, the))          2
45      ((are, considered), (considered, because))          2
46                   ((in, the), (the, community))          2
47            ((is, nothing), (nothing, included))          2
48               ((nothing, is), (is, considered))          2
49              ((considered, there), (there, is))          2
50                         ((do, not), (not, not))          2
51                          ((do, not), (not, we))          2
52                    ((aspects, are), (are, not))          2
53                         ((there, is), (is, no))          1
54                      ((is, no), (no, cultural))          1
55           ((no, cultural), (cultural, history))          1
56            ((cultural, history), (history, in))          1
57                      ((history, in), (in, the))          1
58                        ((in, the), (the, area))          1
59                    ((the, area), (area, there))          1
60                    ((area, there), (there, is))          1
61                       ((there, is), (is, need))          1
62                        ((is, need), (need, to))          1
63                        ((need, to), (to, have))          1
64                     ((to, have), (have, power))          1
65                    ((have, power), (power, in))          1
66                       ((power, in), (in, what))          1

CAC_trigrams_list = list(trigrams(CAC_filtered_tokens))
#print(bigrams_list)
CAC_trigram_counts = Counter(zip(CAC_trigrams_list, CAC_trigrams_list[1:]))
#print(bigram_counts)  
CAC_trigrams = pd.DataFrame(CAC_trigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CAC_trigrams)

                                                    Word  Frequency
0              ((there, is, nothing), (is, nothing, we))          9
1                     ((nothing, we, do), (we, do, not))          9
2                 ((is, nothing, we), (nothing, we, do))          8
3           ((there, is, nothing), (is, nothing, there))          7
4           ((is, nothing, there), (nothing, there, is))          7
5           ((nothing, there, is), (there, is, nothing))          7
6                         ((we, do, not), (do, not, do))          7
7                    ((we, do, not), (do, not, include))          5
8                         ((do, not, do), (not, do, it))          5
9             ((there, is, nothing), (is, nothing, the))          3
10  ((the, cultural, aspects), (cultural, aspects, are))          3
11             ((not, included, we), (included, we, do))          3
12                   ((included, we, do), (we, do, not))          3
13             ((there, is, nothing), (is, nothing, at))          2
14               ((is, nothing, at), (nothing, at, the))          2
15               ((nothing, at, the), (at, the, moment))          2
16              ((they, are, not), (are, not, included))          2
17                  ((if, there, are), (there, are, no))          2
18              ((there, are, no), (are, no, customary))          2
19    ((are, no, customary), (no, customary, practices))          2
20                         ((if, it, is), (it, is, not))          2
21                      ((do, not, do), (not, do, that))          2
22             ((there, is, nothing), (is, nothing, no))          2
23            ((is, nothing, no), (nothing, no, answer))          2
24                         ((not, do, it), (do, it, we))          2
25                      ((we, do, not), (do, not, have))          2
26           ((do, not, include), (not, include, there))          2
27         ((not, include, there), (include, there, is))          2
28          ((include, there, is), (there, is, nothing))          2
29           ((there, is, nothing), (is, nothing, they))          2
30                       ((we, do, not), (do, not, the))          2
31       ((there, is, nothing), (is, nothing, included))          2
32       ((considered, there, is), (there, is, nothing))          2
33                       ((we, do, not), (do, not, not))          2
34                        ((we, do, not), (do, not, we))          2
35       ((cultural, aspects, are), (aspects, are, not))          2
36           ((aspects, are, not), (are, not, included))          2
37           ((are, not, included), (not, included, we))          2
38                 ((there, is, no), (is, no, cultural))          1
39         ((is, no, cultural), (no, cultural, history))          1
40    ((no, cultural, history), (cultural, history, in))          1
41         ((cultural, history, in), (history, in, the))          1
42                 ((history, in, the), (in, the, area))          1
43                 ((in, the, area), (the, area, there))          1
44               ((the, area, there), (area, there, is))          1
45                ((area, there, is), (there, is, need))          1
46                   ((there, is, need), (is, need, to))          1
47                    ((is, need, to), (need, to, have))          1
48                 ((need, to, have), (to, have, power))          1
49                ((to, have, power), (have, power, in))          1
50                ((have, power, in), (power, in, what))          1
51                   ((power, in, what), (in, what, is))          1
52                   ((in, what, is), (what, is, being))          1
53          ((what, is, being), (is, being, formulated))          1
54    ((is, being, formulated), (being, formulated, so))          1
55     ((being, formulated, so), (formulated, so, that))          1
56           ((formulated, so, that), (so, that, there))          1
57                ((so, that, there), (that, there, is))          1
58           ((that, there, is), (there, is, ownership))          1
59      ((there, is, ownership), (is, ownership, there))          1
60     ((is, ownership, there), (ownership, there, are))          1
61           ((ownership, there, are), (there, are, no))          1
62               ((there, are, no), (are, no, cultural))          1
63        ((are, no, cultural), (no, cultural, aspects))          1
64    ((no, cultural, aspects), (cultural, aspects, at))          1
65         ((cultural, aspects, at), (aspects, at, the))          1
66                 ((aspects, at, the), (at, the, time))          1

lemmatizer = WordNetLemmatizer()
CoN = df7['Connection_Nature_Reasons'].str.lower().str.cat(sep=' ')
CoN_words = nltk.tokenize.word_tokenize(CoN)
CoN_filtered_tokens = [word for word in CoN_words if len(CoN_words) >= 4]
CoN_lemmatized_words = [lemmatizer.lemmatize(word) for word in CoN_filtered_tokens]
CoN_token_counts = Counter(CoN_lemmatized_words)
CoN_columns = pd.DataFrame(CoN_token_counts.most_common(67),
columns = ['Word', 'Frequency'])
#print(CoN_columns)

CoN_bigrams_list = list(bigrams(CoN_filtered_tokens))
#print(bigrams_list)
CoN_bigram_counts = Counter(zip(CoN_bigrams_list, CoN_bigrams_list[1:]))
#print(bigram_counts)  
CoN_bigrams = pd.DataFrame(CoN_bigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CoN_bigrams)

                                           Word  Frequency
0                  ((source, of), (of, income))         11
1                   ((a, source), (source, of))          9
2                      ((as, well), (well, as))          5
3                       ((they, are), (are, a))          5
4                       ((are, a), (a, source))          5
5                ((of, income), (income, they))          3
6        ((the, natural), (natural, resources))          3
7              ((the, animals), (animals, can))          2
8                   ((animals, can), (can, be))          2
9                       ((can, be), (be, used))          2
10        ((the, future), (future, generation))          2
11                ((income, they), (they, are))          2
12     ((future, generation), (generation, to))          2
13               ((them, source), (source, of))          2
14                          ((it, is), (is, a))          2
15                       ((is, a), (a, source))          2
16                    ((there, is), (is, need))          2
17                     ((is, need), (need, to))          2
18                    ((are, the), (the, ones))          2
19               ((look, after), (after, them))          2
20             ((source, of), (of, livelihood))          2
21                ((of, income), (income, the))          2
22                       ((help, us), (us, in))          2
23                ((are, source), (source, of))          2
24                   ((source, of), (of, food))          2
25                     ((of, food), (food, as))          2
26                     ((food, as), (as, well))          2
27                        ((a, lot), (lot, of))          2
28                ((thats, where), (where, we))          2
29                     ((where, we), (we, get))          2
30                     ((be, used), (used, by))          1
31                      ((used, by), (by, the))          1
32                   ((by, the), (the, future))          1
33     ((future, generation), (generation, as))          1
34               ((generation, as), (as, well))          1
35                    ((as, well), (well, how))          1
36                    ((well, how), (how, the))          1
37                 ((how, the), (the, animals))          1
38                     ((be, used), (used, as))          1
39                       ((used, as), (as, an))          1
40                    ((as, an), (an, example))          1
41               ((an, example), (example, on))          1
42                   ((example, on), (on, how))          1
43                   ((on, how), (how, people))          1
44            ((how, people), (people, should))          1
45           ((people, should), (should, lead))          1
46              ((should, lead), (lead, their))          1
47               ((lead, their), (their, life))          1
48             ((their, life), (life, through))          1
49    ((life, through), (through, experiments))          1
50  ((through, experiments), (experiments, he))          1
51             ((experiments, he), (he, gains))          1
52            ((he, gains), (gains, knowledge))          1
53        ((gains, knowledge), (knowledge, on))          1
54                 ((knowledge, on), (on, the))          1
55               ((on, the), (the, management))          1
56        ((the, management), (management, of))          1
57             ((management, of), (of, forest))          1
58                 ((of, forest), (forest, to))          1
59                    ((forest, to), (to, use))          1
60                     ((to, use), (use, some))          1
61                    ((use, some), (some, of))          1
62                     ((some, of), (of, them))          1
63                    ((of, them), (them, for))          1
64                 ((them, for), (for, income))          1
65              ((for, income), (income, they))          1
66              ((they, are), (are, important))          1

CoN_trigrams_list = list(trigrams(CoN_filtered_tokens))
#print(bigrams_list)
CoN_trigram_counts = Counter(zip(CoN_trigrams_list, CoN_trigrams_list[1:]))
#print(bigram_counts)  
CoN_trigrams = pd.DataFrame(CoN_trigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CoN_trigrams)

                                                          Word  Frequency
0                      ((a, source, of), (source, of, income))          6
1                           ((they, are, a), (are, a, source))          5
2                          ((are, a, source), (a, source, of))          5
3                   ((source, of, income), (of, income, they))          3
4                    ((the, animals, can), (animals, can, be))          2
5                        ((animals, can, be), (can, be, used))          2
6                   ((them, source, of), (source, of, income))          2
7                               ((it, is, a), (is, a, source))          2
8                           ((is, a, source), (a, source, of))          2
9                          ((there, is, need), (is, need, to))          2
10                 ((a, source, of), (source, of, livelihood))          2
11                   ((source, of, income), (of, income, the))          2
12                        ((source, of, food), (of, food, as))          2
13                          ((of, food, as), (food, as, well))          2
14                          ((food, as, well), (as, well, as))          2
15                      ((thats, where, we), (where, we, get))          2
16                           ((can, be, used), (be, used, by))          1
17                           ((be, used, by), (used, by, the))          1
18                        ((used, by, the), (by, the, future))          1
19              ((by, the, future), (the, future, generation))          1
20       ((the, future, generation), (future, generation, as))          1
21          ((future, generation, as), (generation, as, well))          1
22                   ((generation, as, well), (as, well, how))          1
23                         ((as, well, how), (well, how, the))          1
24                     ((well, how, the), (how, the, animals))          1
25                  ((how, the, animals), (the, animals, can))          1
26                           ((can, be, used), (be, used, as))          1
27                            ((be, used, as), (used, as, an))          1
28                         ((used, as, an), (as, an, example))          1
29                      ((as, an, example), (an, example, on))          1
30                     ((an, example, on), (example, on, how))          1
31                     ((example, on, how), (on, how, people))          1
32                  ((on, how, people), (how, people, should))          1
33             ((how, people, should), (people, should, lead))          1
34             ((people, should, lead), (should, lead, their))          1
35                ((should, lead, their), (lead, their, life))          1
36               ((lead, their, life), (their, life, through))          1
37      ((their, life, through), (life, through, experiments))          1
38  ((life, through, experiments), (through, experiments, he))          1
39      ((through, experiments, he), (experiments, he, gains))          1
40          ((experiments, he, gains), (he, gains, knowledge))          1
41            ((he, gains, knowledge), (gains, knowledge, on))          1
42              ((gains, knowledge, on), (knowledge, on, the))          1
43               ((knowledge, on, the), (on, the, management))          1
44              ((on, the, management), (the, management, of))          1
45           ((the, management, of), (management, of, forest))          1
46                ((management, of, forest), (of, forest, to))          1
47                       ((of, forest, to), (forest, to, use))          1
48                        ((forest, to, use), (to, use, some))          1
49                          ((to, use, some), (use, some, of))          1
50                         ((use, some, of), (some, of, them))          1
51                         ((some, of, them), (of, them, for))          1
52                      ((of, them, for), (them, for, income))          1
53                  ((them, for, income), (for, income, they))          1
54                  ((for, income, they), (income, they, are))          1
55               ((income, they, are), (they, are, important))          1
56              ((they, are, important), (are, important, in))          1
57                ((are, important, in), (important, in, our))          1
58                    ((important, in, our), (in, our, lifes))          1
59                      ((in, our, lifes), (our, lifes, like))          1
60                  ((our, lifes, like), (lifes, like, trees))          1
61                 ((lifes, like, trees), (like, trees, they))          1
62               ((like, trees, they), (trees, they, provide))          1
63             ((trees, they, provide), (they, provide, home))          1
64               ((they, provide, home), (provide, home, for))          1
65                ((provide, home, for), (home, for, animals))          1
66                  ((home, for, animals), (for, animals, as))          1

lemmatizer = WordNetLemmatizer()
CLE = df7['Change_Livelihood_Easy_Reasons'].str.lower().str.cat(sep=' ')
CLE_words = nltk.tokenize.word_tokenize(CLE)
CLE_filtered_tokens = [word for word in CLE_words if len(CLE_words) >= 4]
CLE_lemmatized_words = [lemmatizer.lemmatize(word) for word in CLE_filtered_tokens]
CLE_token_counts = Counter(CLE_lemmatized_words)
CLE_columns = pd.DataFrame(CLE_token_counts.most_common(67),
columns = ['Word', 'Frequency'])
#print(CLE_columns)

CLE_bigrams_list = list(bigrams(CLE_filtered_tokens))
#print(bigrams_list)
CLE_bigram_counts = Counter(zip(CLE_bigrams_list, CLE_bigrams_list[1:]))
#print(bigram_counts)  
CLE_bigrams = pd.DataFrame(CLE_bigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CLE_bigrams)

                                         Word  Frequency
0                    ((as, long), (long, as))         14
1                         ((it, is), (is, a))          7
2           ((to, change), (change, because))          6
3                   ((long, as), (as, there))          6
4                  ((need, to), (to, change))          5
5                  ((we, can), (can, change))          5
6                ((we, depend), (depend, on))          5
7                       ((we, do), (do, not))          5
8                   ((there, is), (is, need))          4
9      ((the, natural), (natural, resources))          4
10                   ((as, well), (well, as))          4
11                   ((it, is), (is, easier))          4
12                ((if, there), (there, are))          4
13                 ((as, there), (there, is))          4
14                   ((do, not), (not, have))          4
15                  ((is, a), (a, challenge))          4
16         ((of, climate), (climate, change))          4
17                     ((thus, it), (it, is))          4
18                ((can, not), (not, change))          4
19                 ((if, there), (there, is))          3
20             ((can, change), (change, the))          3
21                   ((is, need), (need, to))          3
22                      ((long, as), (as, i))          3
23                       ((as, i), (i, have))          3
24               ((source, of), (of, income))          3
25                     ((that, we), (we, do))          3
26                   ((is, not), (not, easy))          3
27                       ((i, do), (do, not))          3
28           ((for, our), (our, livelihoods))          3
29                     ((it, can), (can, be))          3
30                     ((long, as), (as, we))          3
31                     ((it, is), (is, easy))          3
32            ((difficult, to), (to, change))          3
33                   ((change, it), (it, is))          3
34               ((to, change), (change, to))          2
35               ((change, is), (is, easier))          2
36          ((is, easier), (easier, because))          2
37               ((to, change), (change, if))          2
38                ((change, if), (if, there))          2
39                 ((can, be), (be, changed))          2
40    ((from, natural), (natural, resources))          2
41              ((not, depend), (depend, on))          2
42                  ((depend, on), (on, the))          2
43                ((on, the), (the, natural))          2
44              ((have, money), (money, for))          2
45  ((other, livelihoods), (livelihoods, we))          2
46                ((a, source), (source, of))          2
47                ((the, world), (world, is))          2
48                   ((we, need), (need, to))          2
49                ((there, are), (are, some))          2
50         ((thus, changing), (changing, is))          2
51                    ((do, not), (not, use))          2
52     ((use, natural), (natural, resources))          2
53                    ((can, not), (not, be))          2
54                  ((so, that), (that, the))          2
55                  ((i, have), (have, some))          2
56         ((change, because), (because, of))          2
57         ((to, climate), (climate, change))          2
58               ((change, no), (no, answer))          2
59    ((the, livelihoods), (livelihoods, we))          2
60                 ((depend, on), (on, them))          2
61        ((because, the), (the, livelihood))          2
62               ((resources, we), (we, can))          2
63                    ((that, we), (we, can))          2
64                     ((we, can), (can, do))          2
65                   ((so, that), (that, we))          2
66                     ((us, act), (act, in))          2

CLE_trigrams_list = list(trigrams(CLE_filtered_tokens))
#print(bigrams_list)
CLE_trigram_counts = Counter(zip(CLE_trigrams_list, CLE_trigrams_list[1:]))
#print(bigram_counts)  
CLE_trigrams = pd.DataFrame(CLE_trigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CLE_trigrams)

                                                    Word  Frequency
0                    ((as, long, as), (long, as, there))          6
1                   ((long, as, there), (as, there, is))          4
2                      ((it, is, a), (is, a, challenge))          4
3                ((we, can, change), (can, change, the))          3
4                    ((there, is, need), (is, need, to))          3
5                   ((is, need, to), (need, to, change))          3
6                        ((as, long, as), (long, as, i))          3
7                         ((long, as, i), (as, i, have))          3
8                       ((we, do, not), (do, not, have))          3
9                       ((as, long, as), (long, as, we))          3
10                ((need, to, change), (to, change, to))          2
11         ((change, is, easier), (is, easier, because))          2
12               ((to, change, if), (change, if, there))          2
13                ((change, if, there), (if, there, is))          2
14       ((on, the, natural), (the, natural, resources))          2
15               ((a, source, of), (source, of, income))          2
16        ((to, change, because), (change, because, of))          2
17             ((resources, we, can), (we, can, change))          2
18                 ((do, not, have), (not, have, money))          2
19              ((is, a, challenge), (a, challenge, to))          2
20         ((a, challenge, to), (challenge, to, change))          2
21              ((we, only, depend), (only, depend, on))          2
22              ((livelihood, as, long), (as, long, as))          2
23                      ((long, as, we), (as, we, have))          2
24                       ((thus, i, can), (i, can, not))          2
25                      ((it, as, long), (as, long, as))          2
26                 ((long, as, there), (as, there, are))          2
27                      ((thus, it, is), (it, is, easy))          2
28                      ((it, is, easy), (is, easy, to))          2
29                  ((is, easy, to), (easy, to, change))          2
30                      ((it, is, not), (is, not, easy))          2
31                   ((is, not, easy), (not, easy, but))          2
32                   ((not, easy, but), (easy, but, it))          2
33                      ((easy, but, it), (but, it, is))          2
34       ((because, of, climate), (of, climate, change))          2
35                         ((thus, it, is), (it, is, a))          2
36                  ((change, as, long), (as, long, as))          2
37                       ((given, to, us), (to, us, by))          2
38                  ((it, is, easier), (is, easier, as))          2
39                ((is, easier, as), (easier, as, long))          2
40                  ((easier, as, long), (as, long, as))          2
41                     ((as, long, as), (long, as, you))          2
42                     ((if, the, ses), (the, ses, are))          1
43                  ((the, ses, are), (ses, are, taken))          1
44               ((ses, are, taken), (are, taken, care))          1
45               ((are, taken, care), (taken, care, of))          1
46                 ((taken, care, of), (care, of, they))          1
47                   ((care, of, they), (of, they, can))          1
48              ((of, they, can), (they, can, increase))          1
49          ((they, can, increase), (can, increase, in))          1
50         ((can, increase, in), (increase, in, number))          1
51           ((increase, in, number), (in, number, and))          1
52             ((in, number, and), (number, and, bring))          1
53          ((number, and, bring), (and, bring, income))          1
54          ((and, bring, income), (bring, income, the))          1
55            ((bring, income, the), (income, the, ses))          1
56                 ((income, the, ses), (the, ses, can))          1
57                     ((the, ses, can), (ses, can, be))          1
58                 ((ses, can, be), (can, be, depleted))          1
59           ((can, be, depleted), (be, depleted, thus))          1
60       ((be, depleted, thus), (depleted, thus, there))          1
61        ((depleted, thus, there), (thus, there, need))          1
62              ((thus, there, need), (there, need, to))          1
63               ((there, need, to), (need, to, change))          1
64         ((to, change, to), (change, to, agriculture))          1
65  ((change, to, agriculture), (to, agriculture, like))          1
66  ((to, agriculture, like), (agriculture, like, goat))          1

lemmatizer = WordNetLemmatizer()
CLT = df7['Contributor_Landscape_Transformation_Reasons'].str.lower().str.cat(sep=' ')
CLT_words = nltk.tokenize.word_tokenize(CLT)
CLT_filtered_tokens = [word for word in CLT_words if len(CLT_words) >= 4]
CLT_lemmatized_words = [lemmatizer.lemmatize(word) for word in CLT_filtered_tokens]
CLT_token_counts = Counter(CLT_lemmatized_words)
CLT_columns = pd.DataFrame(CLT_token_counts.most_common(67),
columns = ['Word', 'Frequency'])
#print(CLT_columns)

CLT_bigrams_list = list(bigrams(CLT_filtered_tokens))
#print(bigrams_list)
CLT_bigram_counts = Counter(zip(CLT_bigrams_list, CLT_bigrams_list[1:]))
#print(bigram_counts)  
CLT_bigrams = pd.DataFrame(CLT_bigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CLT_bigrams)

                                               Word  Frequency
0                          ((as, well), (well, as))          8
1                             ((a, lot), (lot, of))          6
2                      ((source, of), (of, income))          5
3                           ((there, is), (is, no))          4
4                        ((is, the), (the, source))          4
5                     ((the, source), (source, of))          4
6                 ((the, cutting), (cutting, down))          4
7                         ((a, huge), (huge, area))          4
8                   ((trees, for), (for, charcoal))          4
9                         ((lot, of), (of, things))          4
10                  ((the, fire), (fire, destroys))          4
11                ((it, destroys), (destroys, the))          4
12                      ((when, the), (the, trees))          4
13                     ((the, trees), (trees, are))          4
14                       ((trees, are), (are, cut))          4
15                     ((than, the), (the, others))          3
16                  ((the, burning), (burning, of))          3
17                              ((it, is), (is, a))          3
18                            ((it, is), (is, the))          3
19                    ((cutting, down), (down, of))          3
20                          ((that, is), (is, the))          3
21                          ((well, as), (as, the))          3
22                     ((the, trees), (trees, get))          3
23                 ((cutting, trees), (trees, for))          3
24                             ((are, a), (a, lot))          3
25                        ((destroys, a), (a, lot))          3
26              ((fire, destroys), (destroys, the))          3
27                ((destroys, the), (the, habitat))          3
28                    ((the, trees), (trees, have))          2
29                       ((requires, a), (a, huge))          2
30                        ((a, huge), (huge, land))          2
31  ((the, agriculture), (agriculture, activities))          2
32              ((that, destroys), (destroys, the))          2
33                            ((it, is), (is, for))          2
34    ((for, agriculture), (agriculture, purposes))          2
35                ((brings, income), (income, the))          2
36                        ((the, land), (land, is))          2
37                    ((the, trees), (trees, that))          2
38                           ((is, a), (a, source))          2
39                      ((a, source), (source, of))          2
40                       ((source, of), (of, food))          2
41                         ((in, the), (the, area))          2
42                   ((of, income), (income, some))          2
43                       ((the, soil), (soil, the))          2
44                ((change, the), (the, landscape))          2
45             ((the, landscape), (landscape, the))          2
46                   ((huge, chucks), (chucks, of))          2
47                        ((of, land), (land, for))          2
48                      ((for, a), (a, livelihood))          2
49             ((a, livelihood), (livelihood, the))          2
50                ((the, animals), (animals, will))          2
51                   ((animals, will), (will, not))          2
52                       ((will, not), (not, have))          2
53                   ((cutting, tree), (tree, for))          2
54                   ((tree, for), (for, charcoal))          2
55                      ((it, is), (is, difficult))          2
56              ((is, difficult), (difficult, for))          2
57                     ((the, trees), (trees, dry))          2
58                      ((when, they), (they, are))          2
59                      ((they, are), (are, burnt))          2
60              ((the, fertility), (fertility, of))          2
61                     ((fertility, of), (of, the))          2
62                         ((of, the), (the, soil))          2
63                  ((the, cutting), (cutting, of))          2
64                     ((cutting, of), (of, trees))          2
65                      ((of, trees), (trees, for))          2
66           ((burning, destroys), (destroys, the))          2

CLT_trigrams_list = list(trigrams(CLT_filtered_tokens))
#print(bigrams_list)
CLT_trigram_counts = Counter(zip(CLT_trigrams_list, CLT_trigrams_list[1:]))
#print(bigram_counts)  
CLT_trigrams = pd.DataFrame(CLT_trigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(CLT_trigrams)

                                                           Word  Frequency
0                        ((is, the, source), (the, source, of))          4
1                   ((the, cutting, down), (cutting, down, of))          3
2                             ((as, well, as), (well, as, the))          3
3                             ((a, lot, of), (lot, of, things))          3
4                            ((destroys, a, lot), (a, lot, of))          3
5                       ((when, the, trees), (the, trees, are))          3
6                        ((the, trees, are), (trees, are, cut))          3
7                        ((requires, a, huge), (a, huge, land))          2
8                                ((it, is, a), (is, a, source))          2
9                            ((is, a, source), (a, source, of))          2
10                    ((the, source, of), (source, of, income))          2
11                   ((source, of, income), (of, income, some))          2
12                         ((that, is, the), (is, the, source))          2
13                 ((for, a, livelihood), (a, livelihood, the))          2
14                 ((the, animals, will), (animals, will, not))          2
15                    ((animals, will, not), (will, not, have))          2
16                ((cutting, tree, for), (tree, for, charcoal))          2
17                  ((it, is, difficult), (is, difficult, for))          2
18                      ((when, they, are), (they, are, burnt))          2
19                 ((the, fertility, of), (fertility, of, the))          2
20                      ((fertility, of, the), (of, the, soil))          2
21                   ((the, cutting, of), (cutting, of, trees))          2
22                     ((cutting, of, trees), (of, trees, for))          2
23                   ((of, trees, for), (trees, for, charcoal))          2
24              ((cutting, trees, for), (trees, for, charcoal))          2
25                             ((there, are, a), (are, a, lot))          2
26                                ((are, a, lot), (a, lot, of))          2
27                     ((the, burning, of), (burning, of, the))          2
28               ((the, fire, destroys), (fire, destroys, the))          2
29                         ((trees, are, cut), (are, cut, for))          2
30                      ((are, cut, for), (cut, for, charcoal))          2
31                         ((a, huge, area), (huge, area, and))          2
32                      ((huge, area, and), (area, and, kills))          2
33            ((the, rainfall, will), (rainfall, will, reduce))          2
34            ((to, protect, the), (protect, the, environment))          2
35               ((destroys, the, habitat), (the, habitat, of))          2
36     ((it, destroys, everything), (destroys, everything, on))          2
37          ((destroys, everything, on), (everything, on, its))          2
38                    ((the, trees, have), (trees, have, been))          1
39                ((trees, have, been), (have, been, depleted))          1
40              ((have, been, depleted), (been, depleted, and))          1
41                 ((been, depleted, and), (depleted, and, it))          1
42                        ((depleted, and, it), (and, it, has))          1
43                          ((and, it, has), (it, has, caused))          1
44                  ((it, has, caused), (has, caused, reduced))          1
45        ((has, caused, reduced), (caused, reduced, rainfall))          1
46  ((caused, reduced, rainfall), (reduced, rainfall, because))          1
47       ((reduced, rainfall, because), (rainfall, because, i))          1
48                 ((rainfall, because, i), (because, i, have))          1
49                          ((because, i, have), (i, have, to))          1
50                           ((i, have, to), (have, to, clear))          1
51                        ((have, to, clear), (to, clear, the))          1
52                       ((to, clear, the), (clear, the, land))          1
53                       ((clear, the, land), (the, land, for))          1
54                 ((the, land, for), (land, for, agriculture))          1
55           ((land, for, agriculture), (for, agriculture, to))          1
56           ((for, agriculture, to), (agriculture, to, plant))          1
57               ((agriculture, to, plant), (to, plant, crops))          1
58                  ((to, plant, crops), (plant, crops, hence))          1
59                 ((plant, crops, hence), (crops, hence, the))          1
60               ((crops, hence, the), (hence, the, landscape))          1
61          ((hence, the, landscape), (the, landscape, change))          1
62       ((the, landscape, change), (landscape, change, there))          1
63         ((landscape, change, there), (change, there, would))          1
64                  ((change, there, would), (there, would, a))          1
65                   ((there, would, a), (would, a, reduction))          1
66                  ((would, a, reduction), (a, reduction, in))          1

lemmatizer = WordNetLemmatizer()
LDL = df7['Landscape_Depeneded_Livelihood_Reasons'].str.lower().str.cat(sep=' ')
LDL_words = nltk.tokenize.word_tokenize(LDL)
LDL_filtered_tokens = [word for word in LDL_words if len(LDL_words) >= 4]
LDL_lemmatized_words = [lemmatizer.lemmatize(word) for word in LDL_filtered_tokens]
LDL_token_counts = Counter(LDL_lemmatized_words)
LDL_columns = pd.DataFrame(LDL_token_counts.most_common(67),
columns = ['Word', 'Frequency'])
#print(LDL_columns)

LDL_bigrams_list = list(bigrams(LDL_filtered_tokens))
#print(bigrams_list)
LDL_bigram_counts = Counter(zip(LDL_bigrams_list, LDL_bigrams_list[1:]))
#print(bigram_counts)  
LDL_bigrams = pd.DataFrame(LDL_bigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(LDL_bigrams)

                                    Word  Frequency
0               ((where, we), (we, get))         17
1          ((thats, where), (where, we))         16
2               ((we, get), (get, food))         16
3            ((a, source), (source, of))         12
4              ((water, is), (is, life))         11
5            ((thats, were), (were, we))         10
6                ((were, we), (we, get))          9
7            ((we, are), (are, farmers))          9
8               ((as, well), (well, as))          7
9           ((get, food), (food, crops))          7
10          ((source, of), (of, income))          6
11                ((is, a), (a, source))          6
12               ((we, get), (get, our))          6
13       ((food, crops), (crops, thats))          6
14          ((our, food), (food, crops))          6
15            ((is, the), (the, source))          5
16         ((the, source), (source, of))          5
17            ((source, of), (of, food))          5
18           ((of, food), (food, crops))          5
19             ((that, is), (is, where))          5
20            ((is, where), (where, we))          5
21      ((crops, thats), (thats, where))          5
22      ((source, of), (of, livelihood))          4
23               ((it, is), (is, where))          4
24         ((food, crops), (crops, and))          4
25         ((crops, and), (and, income))          4
26             ((get, our), (our, food))          4
27    ((are, farmers), (farmers, thats))          4
28                   ((it, is), (is, a))          3
29           ((is, life), (life, water))          3
30          ((life, water), (water, is))          3
31            ((source, of), (of, life))          3
32      ((for, our), (our, livelihoods))          3
33        ((food, crops), (crops, come))          3
34         ((crops, come), (come, from))          3
35              ((were, we), (we, grow))          3
36             ((is, life), (life, and))          3
37            ((crops, for), (for, our))          3
38        ((comes, from), (from, thats))          3
39            ((in, the), (the, forest))          3
40        ((where, we), (we, cultivate))          3
41          ((we, depend), (depend, on))          3
42       ((are, farmers), (farmers, by))          3
43         ((farmers, by), (by, nature))          3
44       ((crops, thats), (thats, were))          3
45       ((from, thats), (thats, where))          3
46            ((get, food), (food, for))          3
47              ((that, is), (is, were))          3
48          ((food, crops), (crops, we))          3
49    ((farmers, thats), (thats, where))          3
50           ((for, a), (a, livelihood))          2
51  ((a, livelihood), (livelihood, the))          2
52               ((there, is), (is, no))          2
53                 ((that, is), (is, a))          2
54     ((food, for), (for, consumption))          2
55             ((thats, a), (a, source))          2
56      ((income, comes), (comes, from))          2
57               ((for, a), (a, living))          2
58           ((a, living), (living, it))          2
59            ((it, helps), (helps, us))          2
60      ((from, farming), (farming, we))          2
61        ((we, obtain), (obtain, food))          2
62           ((where, our), (our, food))          2
63            ((if, there), (there, is))          2
64                ((as, a), (a, source))          2
65               ((they, are), (are, a))          2
66               ((are, a), (a, source))          2

LDL_trigrams_list = list(trigrams(LDL_filtered_tokens))
#print(bigrams_list)
LDL_trigram_counts = Counter(zip(LDL_trigrams_list, LDL_trigrams_list[1:]))
#print(bigram_counts)  
LDL_trigrams = pd.DataFrame(LDL_trigram_counts.most_common(67),
columns = ['Word', 'Frequency'])
print(LDL_trigrams)

                                                        Word  Frequency
0                     ((thats, where, we), (where, we, get))         14
1                        ((where, we, get), (we, get, food))         11
2                      ((we, get, food), (get, food, crops))          7
3                         ((is, a, source), (a, source, of))          6
4                       ((thats, were, we), (were, we, get))          6
5                     ((is, the, source), (the, source, of))          5
6                    ((source, of, food), (of, food, crops))          5
7                         ((were, we, get), (we, get, food))          4
8                         ((where, we, get), (we, get, our))          4
9                         ((we, get, our), (get, our, food))          4
10             ((food, crops, thats), (crops, thats, where))          4
11               ((crops, thats, where), (thats, where, we))          4
12               ((we, are, farmers), (are, farmers, thats))          4
13                   ((a, source, of), (source, of, income))          3
14                            ((it, is, a), (is, a, source))          3
15                    ((water, is, life), (is, life, water))          3
16                    ((is, life, water), (life, water, is))          3
17                     ((a, source, of), (source, of, food))          3
18                ((food, crops, and), (crops, and, income))          3
19                ((food, crops, come), (crops, come, from))          3
20                     ((thats, were, we), (were, we, grow))          3
21                      ((water, is, life), (is, life, and))          3
22                      ((that, is, where), (is, where, we))          3
23                  ((we, are, farmers), (are, farmers, by))          3
24               ((are, farmers, by), (farmers, by, nature))          3
25                       ((we, get, food), (get, food, for))          3
26                    ((get, our, food), (our, food, crops))          3
27              ((for, a, livelihood), (a, livelihood, the))          2
28                 ((the, source, of), (source, of, income))          2
29                          ((that, is, a), (is, a, source))          2
30                     ((thats, a, source), (a, source, of))          2
31                       ((for, a, living), (a, living, it))          2
32                        ((as, a, source), (a, source, of))          2
33                        ((they, are, a), (are, a, source))          2
34                       ((are, a, source), (a, source, of))          2
35  ((source, of, traditional), (of, traditional, medicine))          2
36                ((crops, come, from), (come, from, thats))          2
37                      ((as, well, as), (well, as, income))          2
38                          ((life, and, it), (and, it, is))          2
39                     ((were, we, grow), (we, grow, crops))          2
40                    ((life, water, is), (water, is, life))          2
41                         ((as, well, as), (well, as, the))          2
42                      ((is, where, we), (where, we, farm))          2
43                     ((that, where, we), (where, we, get))          2
44                 ((is, where, we), (where, we, cultivate))          2
45                     ((we, depend, on), (depend, on, the))          2
46              ((food, crops, thats), (crops, thats, were))          2
47              ((comes, from, thats), (from, thats, where))          2
48                ((from, thats, where), (thats, where, we))          2
49                   ((get, food, for), (food, for, eating))          2
50                        ((that, is, were), (is, were, we))          2
51                         ((is, were, we), (were, we, get))          2
52                  ((animals, graze, in), (graze, in, the))          2
53           ((are, farmers, thats), (farmers, thats, were))          2
54                         ((were, we, get), (we, get, our))          2
55                ((our, food, crops), (food, crops, thats))          2
56                  ((get, food, crops), (food, crops, and))          2
57                 ((crops, thats, were), (thats, were, we))          2
58                   ((get, food, crops), (food, crops, we))          2
59                     ((food, crops, we), (crops, we, are))          2
60                    ((crops, we, are), (we, are, farmers))          2
61          ((are, farmers, thats), (farmers, thats, where))          2
62             ((farmers, thats, where), (thats, where, we))          2
63                     ((from, we, are), (we, are, farmers))          2
64             ((he, cultivates, a), (cultivates, a, large))          1
65                ((cultivates, a, large), (a, large, area))          1
66                     ((a, large, area), (large, area, of))          1

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11. Specific Variables

11.1 Main Project and Cultural Practices

The responses are grouped in line with the main projects There is also the grouping of responses of those that agreed and storngly agreed in one dataframe as well as those whose responses were strongly disagreed and disagreed
The text responses in form of reasons are tokenized and lammentized

CPH_grouped = df2.groupby('Name_Main_Project')['Cultural_Practices_Hinder'].value_counts(dropna=False)

CPH_grouped

Name_Main_Project                     Cultural_Practices_Hinder
EbA_CENTRAL_MUCHINGA_LUAPULA          Strongly_Agree_Likert         2
                                      Strongly_Disagree_Likert      1
Ecosystem Conservation_NORTH_WESTERN  Strongly_Disagree_Likert      5
                                      Strongly_Agree_Likert         2
                                      NaN                           2
                                      Agree_Likert                  1
                                      Disagree_Likert               1
PIN_WESTERN                           Strongly_Disagree_Likert      5
                                      NaN                           1
SCRALA_SOUTHERN_WESTERN_NORTHEN       Disagree_Likert              11
                                      Strongly_Disagree_Likert      9
                                      Agree_Likert                  5
                                      Strongly_Agree_Likert         2
                                      Undecided_Likert              2
                                      NaN                           1
SCReBS_WESTERN                        Strongly_Disagree_Likert     14
                                      Agree_Likert                  1
SCRiKA_LS                             Strongly_Disagree_Likert     20
                                      Disagree_Likert               6
                                      NaN                           6
                                      Agree_Likert                  5
                                      Strongly_Agree_Likert         5
                                      Undecided_Likert              2
TRALARD_LNM                           Strongly_Disagree_Likert     16
                                      Strongly_Agree_Likert        10
                                      Agree_Likert                  8
                                      Disagree_Likert               4
                                      NaN                           2
Name: count, dtype: int64

CPH_grouped1 = pd.DataFrame(CPH_grouped)

CPH_grouped1

		count
Name_Main_Project	Cultural_Practices_Hinder
EbA_CENTRAL_MUCHINGA_LUAPULA	Strongly_Agree_Likert	2
	Strongly_Disagree_Likert	1
Ecosystem Conservation_NORTH_WESTERN	Strongly_Disagree_Likert	5
	Strongly_Agree_Likert	2
	NaN	2
	Agree_Likert	1
	Disagree_Likert	1
PIN_WESTERN	Strongly_Disagree_Likert	5
	NaN	1
SCRALA_SOUTHERN_WESTERN_NORTHEN	Disagree_Likert	11
	Strongly_Disagree_Likert	9
	Agree_Likert	5
	Strongly_Agree_Likert	2
	Undecided_Likert	2
	NaN	1
SCReBS_WESTERN	Strongly_Disagree_Likert	14
	Agree_Likert	1
SCRiKA_LS	Strongly_Disagree_Likert	20
	Disagree_Likert	6
	NaN	6
	Agree_Likert	5
	Strongly_Agree_Likert	5
	Undecided_Likert	2
TRALARD_LNM	Strongly_Disagree_Likert	16
	Strongly_Agree_Likert	10
	Agree_Likert	8
	Disagree_Likert	4
	NaN	2

plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert", "NaN"]
ax = sns.barplot(data = CPH_grouped1, x="count", y="Name_Main_Project", hue="Cultural_Practices_Hinder", hue_order=hue_order, legend=True)
ax.set_title("Figure 13: Number of Responses on cultural Practices Hindering Sustainable Management of SES in the Main Projects", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()

agreement_levels = ["Agree_Likert", "Strongly_Agree_Likert"]
CPH_R = df2[df2["Cultural_Practices_Hinder"].isin(agreement_levels)]

CPH_R1 = CPH_R.drop(CPH_R.columns[[0,1,2,3,4,6,7,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]], axis = 1)
CPH_R1grouped = CPH_R1.groupby('Name_Main_Project')['Cultural_Practices_Hinder']
#CPH_R1

lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in CPH_R1.iterrows():
    CPH_R1_filter_sentence = []
    CPH_R1_sentence = row["Cultural_Practices_Hinder_Reason"]
    if pd.isnull(CPH_R1_sentence):
       continue
    CPH_R1_sentence_cleaned = re.sub(r'[^\w\s]','',CPH_R1_sentence)
    CPH_R1_words = nltk.word_tokenize(CPH_R1_sentence_cleaned)
    CPH_R1_words = [lemmatizer.lemmatize(w) for w in CPH_R1_words if w.lower() not in stop_words]
    CPH_R1_filter_sentence.extend(CPH_R1_words)
    print(CPH_R1_filter_sentence)

['need', 'harvest', 'tree', 'fully', 'grown', 'applies', 'biodiversity']
['crop', 'rotation', 'made', 'land', 'cultivating', 'ensure', 'fertility', 'soil']
['allowed', 'cultivate', 'area', '3', 'year', 'shifting', 'another', 'area']
['rule', 'chief', 'ensure', 'harvesting', 'period', 'followed', 'well', 'period', 'burning', 'bush']
['norm', 'bush', 'burned', 'dry', 'bush', 'completely', 'burn', 'also', 'support', 'quick', 'regermination', 'vegetation', 'tree']
['nothing']
['never', 'heard', 'learnt']
['cutting', 'tree', 'along', 'river', 'allowed']
['act', 'late', 'burning', 'destroys', 'environment', 'people', 'community', 'would', 'want', 'catch', 'Catapilars', 'consumption', 'sale']
['long', 'time', 'ago', 'people', 'poor', 'management', 'natural', 'moment', 'practice', 'maintain', 'biodiversity']
['cultural', 'practice', 'future', 'generation', 'know', 'anything']
['customary', 'practice', 'promote', 'early', 'burning', 'people', 'tend', 'burn', 'late']
['unregulated', 'allocation', 'parcel', 'land', 'forest']
['Ba', 'chipupila', 'customary', 'practice', 'protecting', 'natural', 'resource']
['place', 'grave', 'yard', 'protected', 'deforestation']
['land', 'natural', 'resource', 'located', 'owned', 'traditional', 'authority']
['Chitemene', 'system', 'destroys', 'environment']
['experienced', 'cultural', 'practice', 'interfering', 'forest', 'management']
['old', 'parent', 'taught', 'u', 'protect', 'environment', 'like', 'customary', 'practice']
['rule', 'made', 'customary', 'practice', 'followed']
['place', 'allow', 'cutting', 'tree', 'well', 'cutting', 'fruit', 'bearing', 'tree']
['protection', 'environment', 'resource', 'depleted']
['cutting', 'tree', 'anyhow']
['teach', 'u', 'protect', 'environment', 'preventing', 'Chitemene', 'system']
['always', 'teach', 'people', 'community', 'protect', 'environment']
['normally', 'give', 'rule', 'protect', 'environment']
['traditional', 'method', 'harvesting', 'poaching', 'burning', 'cutting', 'tree']
['people', 'cut', 'tree', 'thus', 'leading', 'wild', 'animal', 'lacking', 'sleep', 'shelter']
['practice', 'called', 'Malende', 'protect', 'certain', 'area', 'prohibit', 'tree', 'cut']
['Hynas', 'eat', 'livestock', 'kill', 'conflict', 'ZAWA', 'Officers']
['ownership', 'land', 'Chief', 'river', 'give', 'power', 'destroy', 'area', 'giving', 'cultivation', 'activity']
['cultural', 'practice', 'prevent', 'rain', 'falling', 'Malende', 'disturbed']
['bad', 'fishing', 'method', 'well', 'people', 'settling', 'game', 'park']
['lack', 'support', 'WDCS', 'CRB', 'local', 'community', 'protect', 'environment', 'license', 'given', 'investor', 'cut', 'tree', 'community', 'benefit', 'sale', 'tree', 'tradition', 'authority', 'benefit']
['people', 'still', 'want', 'lead', 'life', 'hunting', 'use', 'mosquito', 'net', 'catching', 'fish']
['traditional', 'leader', 'prohibit', 'people', 'making', 'decision', 'sell', 'land', 'people', 'lead', 'destruction', 'environment', 'even', 'people', 'agree', 'headman', 'headman', 'say', 'land', 'sell', 'want']
['cultural', 'activity', 'getting', 'root', 'tree', 'medicine', 'destroy', 'tree']
['culture', 'someone', 'live', 'well', 'need', 'cultivate', 'huge', 'parcel', 'land', 'thus', 'leading', 'cutting', 'tree']
['Chiefs', 'asking', 'people', 'stop', 'living', 'along', 'river', 'bank', 'using', 'mosquito', 'net', 'fish', 'community', 'adhearing']

CPH_R1["Cultural_Practices_Hinder_Reason"] = CPH_R1["Cultural_Practices_Hinder_Reason"].fillna("")  
CPH_R1["Cultural_Practices_Hinder_Reason"] = CPH_R1["Cultural_Practices_Hinder_Reason"].astype(str)
CPH_R1_Text = " ".join(CPH_R1["Cultural_Practices_Hinder_Reason"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(CPH_R1_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Cultural Practices Hinder", loc="left", fontsize=20, pad=20)
plt.axis("off")  
plt.show()

agreement_levels = ["Disagree_Likert", "Strongly_Disagree_Likert"]
CPH_R2 = df2[df2["Cultural_Practices_Hinder"].isin(agreement_levels)]

CPH_R3 = CPH_R2.drop(CPH_R2.columns[[0,1,2,3,4,6,7,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]], axis = 1)
CPH_R3grouped = CPH_R3.groupby('Name_Main_Project')['Cultural_Practices_Hinder']
#CPH_R3

lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in CPH_R3.iterrows():
    CPH_R3_filter_sentence = []
    CPH_R3_sentence = row["Cultural_Practices_Hinder_Reason"]
    if pd.isnull(CPH_R3_sentence):
       continue
    CPH_R3_sentence_cleaned = re.sub(r'[^\w\s]','',CPH_R3_sentence)
    CPH_R3_words = nltk.word_tokenize(CPH_R3_sentence_cleaned)
    CPH_R3_words = [lemmatizer.lemmatize(w) for w in CPH_R3_words if w.lower() not in stop_words]
    CPH_R3_filter_sentence.extend(CPH_R3_words)
    print(CPH_R3_filter_sentence)

['never', 'happed', 'life', 'time']
['customary', 'practice', 'hinder', 'sustainable', 'management']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['cultural', 'history', 'within', 'community', 'except', 'governmnet']
['cultural', 'norm']
['nothing']
['nothing']
['nothing']
['customary', 'practice', 'protect', 'environment', 'like', 'way', 'ZAWA', 'protects', 'biodiversity']
['chitemene', 'system', 'customary', 'practice', 'destroys', 'environment']
['cultural', 'practice', 'burn', 'bush', 'certain', 'period', 'harvesting']
['cultural', 'practice']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothinhg']
['traditional', 'practice', 'government', 'guide', 'u']
['nothing']
['nothing']
['nothing', 'SES', 'looked', 'game', 'park', 'officer', 'officer', 'mandated']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['traditional', 'way', 'management', 'stopped', 'government', 'taken', 'management', 'forest', 'biodiversity']
['nothing']
['long', 'time', 'malende', 'used', 'protect', 'tree', 'moment', 'taking', 'place']
['nothing']
['nothing']
['nothing']
['heard', 'anything', 'like']
['nothing', 'know', 'hinder']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['thing']
['nothing']
['nothing']
['nothing']
['Thee', 'nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['hill', 'called', 'Omba', 'release', 'smoke', 'indicating', 'particular', 'year', 'would', 'rainfall', 'evergthing', 'would', 'fine', 'regard', 'rainfall']
['following', 'law']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']
['nothing']

CPH_R3["Cultural_Practices_Hinder_Reason"] = CPH_R3["Cultural_Practices_Hinder_Reason"].fillna("")  
CPH_R3["Cultural_Practices_Hinder_Reason"] = CPH_R3["Cultural_Practices_Hinder_Reason"].astype(str)
CPH_R3_Text = " ".join(CPH_R3["Cultural_Practices_Hinder_Reason"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(CPH_R3_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Cultural Practices Hinder", loc="left", fontsize=20, pad=20)
plt.axis("off")  
plt.show()

11.2 Main Project and Cultural Practices Changed

CPC_grouped = df2.groupby('Name_Main_Project')['Cultural_Practices_Changed'].value_counts(dropna=False)

CPC_grouped

Name_Main_Project                     Cultural_Practices_Changed
EbA_CENTRAL_MUCHINGA_LUAPULA          Strongly_Agree_Likert          1
                                      Strongly_Disagree_Likert       1
                                      Undecided_Likert               1
Ecosystem Conservation_NORTH_WESTERN  NaN                            5
                                      Strongly_Agree_Likert          4
                                      Strongly_Disagree_Likert       2
PIN_WESTERN                           Strongly_Disagree_Likert       4
                                      Strongly_Agree_Likert          1
                                      NaN                            1
SCRALA_SOUTHERN_WESTERN_NORTHEN       Agree_Likert                  11
                                      Disagree_Likert                7
                                      Strongly_Disagree_Likert       4
                                      Undecided_Likert               4
                                      Strongly_Agree_Likert          3
                                      NaN                            1
SCReBS_WESTERN                        Strongly_Disagree_Likert       6
                                      Strongly_Agree_Likert          5
                                      Agree_Likert                   4
SCRiKA_LS                             Agree_Likert                  11
                                      Strongly_Agree_Likert         10
                                      NaN                           10
                                      Strongly_Disagree_Likert       6
                                      Disagree_Likert                5
                                      Undecided_Likert               2
TRALARD_LNM                           Strongly_Disagree_Likert      18
                                      Disagree_Likert               10
                                      Strongly_Agree_Likert          5
                                      Agree_Likert                   3
                                      Undecided_Likert               2
                                      NaN                            2
Name: count, dtype: int64

CPC_grouped1 = pd.DataFrame(CPC_grouped)

plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = CPC_grouped1, x="count", y="Name_Main_Project", hue="Cultural_Practices_Changed", hue_order=hue_order, legend=True)
ax.set_title("Figure 14: Number of Responses on Willingnes to Change Cultural Practices for Sustainable Management of SES in the Main Projects", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()

11.3 Main project and Cultural Aspects Considered

CAC_grouped = df2.groupby('Name_Main_Project')['Cultural_Aspects_Considered'].value_counts(dropna=False)

CAC_grouped1 = pd.DataFrame(CAC_grouped)

plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = CAC_grouped1, x="count", y="Name_Main_Project", hue="Cultural_Aspects_Considered", hue_order=hue_order, legend=True)
ax.set_title("Figure 15: Number of Responses on if Cultural Aspects are Considered for Sustainable Management of SES in the Main Projects", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()

agreement_levels = ["Agree_Likert", "Strongly_Agree_Likert"]
CAC_R = df2[df2["Cultural_Aspects_Considered"].isin(agreement_levels)]
CAC_R1 = CAC_R.drop(CPH_R.columns[[0,1,2,3,4,6,7,8,9,10,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]], axis = 1)
CAC_R1grouped = CAC_R1.groupby('Name_Main_Project')['Cultural_Aspects_Considered']
#CAC_R1

lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in CAC_R1.iterrows():
    CAC_R1_filter_sentence = []
    CAC_R1_sentence = row["Cultural_Aspects_Considered_Reasons"]
    if pd.isnull(CAC_R1_sentence):
       continue
    CAC_R1_sentence_cleaned = re.sub(r'[^\w\s]','',CAC_R1_sentence)
    CAC_R1_words = nltk.word_tokenize(CAC_R1_sentence_cleaned)
    CAC_R1_words = [lemmatizer.lemmatize(w) for w in CAC_R1_words if w.lower() not in stop_words]
    CAC_R1_filter_sentence.extend(CAC_R1_words)
    print(CAC_R1_filter_sentence)

['need', 'power', 'formulated', 'ownership']
['time', 'getting', 'land', 'project', 'come', 'regulation']
['rule', 'along', 'river', 'bank', 'tree', 'need', 'cut']
['sometimes', 'give', 'Chiefs', 'harvest', 'collect', 'produce', 'accounted']
['nothing', 'thing', 'ended', 'long', 'time', 'ago', 'Chiefs', 'would', 'talk', 'spirit', 'protect', 'land']
['included']
['customary', 'practice', 'natural', 'resource', 'get', 'destroyed']
['answer']
['answer']
['done', 'product', 'project', 'work', 'well']
['follow', 'customary', 'practice', 'people', 'burn', 'late', 'inorder']
['livelihood', 'improve']
['follow', 'historical', 'practice', 'forefather']
['cultural', 'practice', 'spririal', 'rite', 'done', 'traditional', 'authority']
['accessing', 'land', 'traditional', 'authority', 'also', 'give', 'instruction', 'put', 'livelihood', 'close', 'water', 'source', 'resource']
['project', 'operate', 'without', 'consulting', 'traditional', 'authority']
['answer']
['Sometimes', 'discus', 'locally', 'animal', 'going', 'looked']
['always', 'follow', 'law', 'taught', 'u']
['destroy', 'environment', 'customary', 'practice']
['changed', 'would', 'like', 'environment', 'protected', 'generation', 'see', 'future']
['Previously', 'people', 'used', 'cut', 'tree', 'anyhow', 'change']
['cultural', 'aspect', 'considered', 'within', 'CFMG']
['Even', 'Bible', 'say', 'forget', 'root']
['always', 'follow', 'told']
['consider', 'project', 'would', 'work', 'well', 'instance', 'Livingstone', 'community', 'destroyed', 'cultural', 'activity', 'northwestern', 'came', 'different', 'region']
['Borehole', 'sank', 'near', 'grave', 'yard', 'funeral', 'community', 'hold', 'meeting', 'village']
['group', 'mission', 'like', 'group', 'give', 'money', 'orphan', 'profit', 'make']
[]
[]
['answer']
['considered', 'thatched', 'roof', 'using', 'pole', 'cattle', 'craw', 'cultural', 'aspect', 'difficult', 'change', 'unless', 'people', 'enough', 'money']
['project', 'bettering', 'life']
['taught']
['traditional', 'leader', 'accept', 'certain', 'project', 'done', 'specific', 'place']
['included', 'induna']
['indunas', 'send', 'representative', 'learn', 'accept', 'project', 'community']

CAC_R1["Cultural_Aspects_Considered_Reasons"] = CAC_R1["Cultural_Aspects_Considered_Reasons"].fillna("")  
CAC_R1["Cultural_Aspects_Considered_Reasons"] = CAC_R1["Cultural_Aspects_Considered_Reasons"].astype(str)
CAC_R1_Text = " ".join(CAC_R1["Cultural_Aspects_Considered_Reasons"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(CAC_R1_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Cultural Aspects Considered", loc="left", fontsize=20, pad=20)
plt.axis("off")  
plt.show()

agreement_levels = ["Disagree_Likert", "Strongly_Disagree_Likert"]
CAC_R2 = df2[df2["Cultural_Aspects_Considered"].isin(agreement_levels)]
CAC_R3 = CAC_R2.drop(CPH_R2.columns[[0,1,2,3,4,6,7,8,9,10,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]], axis = 1)
CAC_R3grouped = CAC_R3.groupby('Name_Main_Project')['Cultural_Aspects_Considered']
#CAC_R3

lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in CAC_R3.iterrows():
    CAC_R3_filter_sentence = []
    CAC_R3_sentence = row["Cultural_Aspects_Considered_Reasons"]
    if pd.isnull(CAC_R3_sentence):
       continue
    CAC_R3_sentence_cleaned = re.sub(r'[^\w\s]','',CAC_R3_sentence)
    CAC_R3_words = nltk.word_tokenize(CAC_R3_sentence_cleaned)
    CAC_R3_words = [lemmatizer.lemmatize(w) for w in CAC_R3_words if w.lower() not in stop_words]
    CAC_R3_filter_sentence.extend(CAC_R3_words)
    print(CAC_R3_filter_sentence)

['cultural', 'history', 'area']
['cultural', 'aspect']
['follow', 'chief', 'government', 'say']
['nothing']
['nothing']
['nothing']
['nothing']
['plan', 'given', 'u', 'TRALARD', 'consideration', 'cultural', 'history', 'formulating', 'project']
['nothing']
['moment', 'follow', 'law', 'government', 'cultural', 'norm']
['nothing']
['ask', 'headman', 'give', 'u', 'place', 'keep', 'goat', 'place', 'acceptable', 'traditional', 'law']
['nothing', 'moment', 'traditional', 'leader', 'spritual', 'activity', 'project']
['include', 'cultural', 'practice']
['project', 'move', 'well', 'headman', 'project', 'otherwise', 'included', 'might', 'problem']
['available']
['consider']
['nothing']
['nothing']
[]
['nothing']
[]
['unneccesary']
['anything']
[]
['nothing']
['include']
[]
['nothing']
['include', 'aspect']
['nothing']
['nothing', 'included']
['Nothing', 'considered']
['nothing']
[]
['Nothing', 'considered']
['nothing']
['included']
['include']
['nothing', 'included']
['nothing']
['included']
['Nothing']
['Nothing']
['nothing']
['control', 'Chief', 'make', 'change']
['nothing', 'included']
['taken', 'consideration']
['project', 'destroyed', 'culture']
['nothing']
[]
['nothing']
['project', 'associated', 'cultural', 'aspect']
['Thee', 'nothing']
['nothing']
[]
['considered']
['nothing']
['nothing']
['nothing']
['project', 'come', 'BRE']
['part', 'project']
[]
[]
['Nothing']
[]
['nothing']
['follow', 'teaching']
['cultural', 'aspect', 'included']
['cultural', 'aspect', 'included']
[]
[]
['included']
['include']
['nothing']
['nothing']
[]

CAC_R3["Cultural_Aspects_Considered_Reasons"] = CAC_R3["Cultural_Aspects_Considered_Reasons"].fillna("")  
CAC_R3["Cultural_Aspects_Considered_Reasons"] = CAC_R3["Cultural_Aspects_Considered_Reasons"].astype(str)
CAC_R3_Text = " ".join(CAC_R3["Cultural_Aspects_Considered_Reasons"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(CAC_R3_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Cultural Aspects Considered", loc="left", fontsize=20, pad=20)
plt.axis("off")  
plt.show()

11.4 Main Project and Measure of Indicators

MI_grouped = df2.groupby('Name_Main_Project')['Measure_Indicators'].value_counts(dropna=False)

MI_grouped1 = pd.DataFrame(MI_grouped)

plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = MI_grouped1, x="count", y="Name_Main_Project", hue="Measure_Indicators", hue_order=hue_order, legend=True)
ax.set_title("Figure 16: Number of Responses on if Measurement of Indicators is important for Sustainable Management of SES in the Main Projects", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()

11.5 Main Project and Livelihood Dependent

LD_grouped = df2.groupby('Name_Main_Project')['Livilihood_Depenedent'].value_counts(dropna=False)

LD_grouped1 = pd.DataFrame(LD_grouped)

plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = LD_grouped1, x="count", y="Name_Main_Project", hue="Livilihood_Depenedent", hue_order=hue_order, legend=True)
ax.set_title("Figure 17: Number of Responses in each of the main Project on if Livelihoods Depend on SES", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()

11.6 Main Project and Livelihood Changed

CL_grouped = df2.groupby('Name_Main_Project')['Change_Livelihood'].value_counts(dropna=False)

CL_grouped1 = pd.DataFrame(CL_grouped)

plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = CL_grouped1, x="count", y="Name_Main_Project", hue="Change_Livelihood", hue_order=hue_order, legend=True)
ax.set_title("Figure 18: Number of Responses in each of the main Project on if Livelihoods can be Changed", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()

11.7 Main Project and Livelihood Changing Easy

CLE_grouped = df2.groupby('Name_Main_Project')['Change_Livelihood_Easy'].value_counts(dropna=False)

CLE_grouped1 = pd.DataFrame(CLE_grouped)

plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = CLE_grouped1, x="count", y="Name_Main_Project", hue="Change_Livelihood_Easy", hue_order=hue_order, legend=True)
ax.set_title("Figure 19: Number of Responses in each of the main Project on if Livelihoods can be Changed Easily", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()

agreement_levels = ["Agree_Likert", "Strongly_Agree_Likert"]
CLE_R = df2[df2["Change_Livelihood_Easy"].isin(agreement_levels)]
CLE_R1 = CLE_R.drop(CLE_R.columns[[0,1,2,3,4,6,7,8,9,10,11,12,13,14,15,16,17,18,19,22,23,24,25,26,27,28,29,30,31,32,33,34]], axis = 1)
CLE_R1grouped = CLE_R1.groupby('Name_Main_Project')['Change_Livelihood_Easy']
#CLE_R1

lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in CLE_R1.iterrows():
    CLE_R1_filter_sentence = []
    CLE_R1_sentence = row["Change_Livelihood_Easy_Reasons"]
    if pd.isnull(CLE_R1_sentence):
       continue
    CLE_R1_sentence_cleaned = re.sub(r'[^\w\s]','',CLE_R1_sentence)
    CLE_R1_words = nltk.word_tokenize(CLE_R1_sentence_cleaned)
    CLE_R1_words = [lemmatizer.lemmatize(w) for w in CLE_R1_words if w.lower() not in stop_words]
    CLE_R1_filter_sentence.extend(CLE_R1_words)
    print(CLE_R1_filter_sentence)

['SES', 'taken', 'care', 'increase', 'number', 'bring', 'income']
['SeS', 'depleted', 'thus', 'need', 'change', 'agriculture', 'like', 'goat', 'rearing']
['Change', 'easier', 'one', 'decides', 'change']
['thing', 'like', 'climate', 'change', 'affecting', 'u', 'thus', 'thought', 'change']
['new', 'improvement', 'like', 'cooking', 'stove', 'change', 'way', 'livelihood']
['destruction', 'SES']
['need', 'change', 'cutting', 'activity']
['need', 'change', 'activity', 'destroy', 'environment']
['changed', 'learning']
['kept', 'well', 'project', 'like', 'chicken', 'goat', 'would', 'help', 'depend', 'natural', 'resource']
['fish', 'pond', 'project']
['also', 'improve', 'livelihood', 'future']
['long', 'money', 'livelihood']
['want', 'venture', 'farming', 'reason', 'shifted', 'place', 'located']
['sub', 'project', 'bee', 'keeping', 'disturbed', 'late', 'burning', 'early', 'burning', 'disturb', 'fire', 'much']
['need', 'change', 'instance', 'depend', 'water', 'Lulimala', 'river', 'dry', 'get', 'water']
['natural', 'resource', 'protected', 'accessed']
['use', 'according', 'accepted', 'norm']
['Yes', 'normally', 'use', 'natural', 'resource', 'somehow']
['environment', 'protected', 'business']
['easier', 'change', 'experience', 'natural', 'resource', 'change', 'accordance', 'climate', 'change']
['change', 'slowly', 'due', 'low', 'performance', 'group']
['depend']
['would', 'difficult', 'time', 'would', 'change', 'gradually']
['need', 'change', 'world', 'developing', 'hence', 'move']
['keeping', 'goat', 'help', 'protecting', 'environment', 'livelihood', 'depend']
['source', 'income']
['government', 'support', 'u', 'turn', 'protect', 'environment']
['people', 'teaching', 'new', 'thing', 'learn']
['thing', 'difficult', 'look', 'instance', 'honey', 'bee', 'mushroom', 'may', 'difficult', 'find']
['protected', 'resource', 'change']
['long', 'help', 'government']
['farming']
['project', 'help', 'u', 'improve', 'livelihood']
['change', 'way', 'depending', 'cutting', 'tree', 'focus', 'producing', 'honey', 'livelihood']
['knowledge', 'make', 'u', 'act', 'certain', 'way', 'currently', 'future', 'might', 'new', 'knowledge', 'would', 'make', 'u', 'act', 'different', 'way', 'initial', 'one']
['earning', 'natural', 'resource', 'develop']
['change', 'easier', 'depent', 'people', 'easily', 'changed']
['livelihood', 'plan', 'small', 'livestock', 'CFMG', 'well', 'garden']
['source', 'earning', 'living']
['management', 'forest', 'capacity', 'buildiing']
['climate', 'change', 'force', 'change', 'livelihood']
['depend', 'domecticated', 'animal']
['long', 'support', 'somewhere']
['resource', 'change', 'livelihood']
['long', 'depend', 'livelihood']
['enough', 'water', 'inland', 'depeneding', 'river', 'shore', 'cultivation', 'purpose', 'livelihood', 'would', 'change']
['Thing', 'evolving', 'thus', 'stagnant']
['long', 'helped', 'altrenative', 'livelihood']
['trying', 'shift', 'make', 'garden', 'livelihood']
['way', 'thing', 'climate', 'change', 'call', 'change']
['long', 'different', 'alternative', 'livelihood']
['Yes', 'depending', 'catle', 'looking', 'thus', 'easy', 'change']
['profit', 'one', 'livelihood', 'change', 'another', 'type', 'livelihood']
['easy', 'important', 'change', 'climate', 'change', 'change', 'without', 'taking', 'alternative']
['livelihood', 'depends', 'farming', 'main', 'activity']
['Changing', 'difficult', 'get', 'used', 'fine']
['always', 'depending', 'farming', 'thus', 'change', 'would', 'lead', 'u', 'access', 'currently']
['always', 'focused', 'agriculture', 'thus', 'little', 'bit', 'difficult', 'change']
['answer']
['would', 'prefer', 'shift', 'gardening', 'activity']
['change', 'lead', 'better', 'life', 'easy']
['guideline', 'assist', 'changing', 'livelihood']
['know', 'livelihood', 'would', 'change', 'might', 'worse', 'current', 'one']
['changed', 'dependency', 'climate', 'change']
['long', 'advantage', 'disadvantage', 'well', 'technology', 'allow', 'done']
['depend', 'forest', 'wetland']
['changing', 'better', 'one']
['Life', 'hard', 'due', 'high', 'cost', 'commodity']
['problem', 'boreholes']
['easy', 'good', 'thing']
['way', 'live', 'adapt', 'environment', 'like', 'climate', 'change', 'thus', 'adapted', 'challenge']
['find', 'someone', 'assist', 'u', 'change']
['knowledge', 'use', 'thing', 'given', 'u', 'government', 'live', 'good', 'life']
['law', 'ask', 'u', 'change']
['long', 'help', 'somewhere']
['easier', 'long', 'time', 'process']
['long', 'use', 'change']
['limited', 'responsibility', 'thus', 'easy', 'change']
['period', 'climate', 'change', 'call', 'different', 'way', 'thing']
['empowered', 'easy']
['use', 'knowledge', 'adquately', 'easier']
['long', 'need', 'change', 'well', 'climate', 'change']
['easier', 'long', 'commitment']
['person', 'make', 'decision', 'looking', 'back', 'done', 'make', 'corrective', 'measure']
['long', 'committed']
['difficult', 'long', 'follow', 'taught', 'change']
['long', 'capacity']

CLE_R1["Change_Livelihood_Easy_Reasons"] = CLE_R1["Change_Livelihood_Easy_Reasons"].fillna("")  
CLE_R1["Change_Livelihood_Easy_Reasons"] = CLE_R1["Change_Livelihood_Easy_Reasons"].astype(str)
CLE_R1_Text = " ".join(CLE_R1["Change_Livelihood_Easy_Reasons"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(CLE_R1_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Change Livelihood Easy", loc="left", fontsize=20, pad=20)
plt.axis("off")  
plt.show()

agreement_levels = ["Disagree_Likert", "Strongly_Disagree_Likert"]
CLE_R2 = df2[df2["Change_Livelihood_Easy"].isin(agreement_levels)]
CLE_R3 = CLE_R2.drop(CLE_R2.columns[[0,1,2,3,4,6,7,8,9,10,11,12,13,14,15,16,17,18,19,22,23,24,25,26,27,28,29,30,31,32,33,34]], axis = 1)
CLE_R3grouped = CLE_R3.groupby('Name_Main_Project')['Change_Livelihood_Easy']
#CLE_R3

lemmatizer=WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
for index, row in CLE_R3.iterrows():
    CLE_R3_filter_sentence = []
    CLE_R3_sentence = row["Change_Livelihood_Easy_Reasons"]
    if pd.isnull(CLE_R3_sentence):
       continue
    CLE_R3_sentence_cleaned = re.sub(r'[^\w\s]','',CLE_R3_sentence)
    CLE_R3_words = nltk.word_tokenize(CLE_R3_sentence_cleaned)
    CLE_R3_words = [lemmatizer.lemmatize(w) for w in CLE_R3_words if w.lower() not in stop_words]
    CLE_R3_filter_sentence.extend(CLE_R3_words)
    print(CLE_R3_filter_sentence)

['livelihood', 'would', 'improve']
['everything', 'use', 'come', 'natural', 'resource', 'like', 'tree', 'building', 'animal', 'protein']
['source', 'income']
['assist', 'adequate', 'water', 'tree', 'cut', 'well', 'future', 'general', 'see', 'natural', 'resource']
['world', 'becoming', 'mordenised', 'thus', 'need', 'adapt', 'current', 'status']
['Thats', 'income', 'come']
['depend', 'natural', 'resource', 'thus', 'changing', 'easy']
['use', 'natural', 'resource']
['depend', 'agriculture', 'natural', 'resource', 'like', 'forest']
['mostly', 'use', 'goat', 'pig', 'livelihood']
['livelihood', 'engage', 'keeping', 'goat', 'fish', 'farming']
['livelihood', 'like', 'keeping', 'goat', 'chicken', 'gardening', 'hiring', 'wedding', 'dress']
['use', 'domesticated', 'animal']
['use']
['thing', 'learning', 'already']
['money', 'protection', 'environment', 'yet', 'money', 'carbon', 'trade', 'waiting', 'long', 'time']
['used']
['changing', 'another', 'lifestyle', 'mean', 'starting', 'new', 'life']
['currently', 'drought', 'thus', 'difficult', 'change']
['natural', 'resource', 'given', 'u', 'God', 'depend', 'root', 'tree', 'medicine', 'fruit']
['one', 'activity', 'farming', 'thus', 'changing', 'difficult']
['livelihood', 'based', 'farming']
['always', 'use', 'product', 'forest']
['livelihood', 'difficult', 'moment', 'climate', 'change']
['problem', 'thus', 'change']
['person', 'change', 'unless', 'person', 'shown']
['manage', 'live', 'without', 'depending', 'forest', 'like', 'craw', 'use', 'tree']
['starting', 'point', 'difficult', 'change', 'need', 'finance']
['livelihood', 'shifting']
['livelihood']
['sure', 'future', 'thus', 'cannaot', 'change']
['challenge']
['firewood', 'depend', 'tree', 'thus', 'challenge', 'change', 'Agriculture', 'mean', 'cutting', 'tree', 'building', 'house', 'depends', 'tree', 'Also', 'piggery', 'project', 'done', 'market', 'thus', 'project', 'effective']
['difficult', 'money', 'pig', 'keeping', 'challenge', 'looking', 'die']
['person', 'leading', 'better', 'life', 'change']
['old', 'age', 'thus', 'change', 'livelihood']
['continue', 'trying']

CLE_R3["Change_Livelihood_Easy_Reasons"] = CLE_R3["Change_Livelihood_Easy_Reasons"].fillna("")  
CLE_R3["Change_Livelihood_Easy_Reasons"] = CLE_R3["Change_Livelihood_Easy_Reasons"].astype(str)
CLE_R3_Text = " ".join(CLE_R1["Change_Livelihood_Easy_Reasons"])
wordcloud = WordCloud(background_color = "white", width = 1000, height = 400).generate(CLE_R3_Text)
plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.title("Figure 20: Change Livelihood Easy", loc="left", fontsize=20, pad=20)
plt.axis("off")  
plt.show()

11.8 Main Project and Ecosystem Service Reduction

ESR_grouped = df2.groupby('Name_Main_Project')['Ecosystem_Services_Reduced'].value_counts(dropna=False)

ESR_grouped1 = pd.DataFrame(ESR_grouped)

plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = ESR_grouped1, x="count", y="Name_Main_Project", hue="Ecosystem_Services_Reduced", hue_order=hue_order, legend=True)
ax.set_title("Figure 20: Number of Responses in each of the main Project on if Ecosystem services have Reduced", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()

11.9 Main project and Deforestation Increase

DI_grouped = df2.groupby('Name_Main_Project')['Deforestaion_Increased'].value_counts(dropna=False)

DI_grouped1 = pd.DataFrame(DI_grouped)

plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = DI_grouped1, x="count", y="Name_Main_Project", hue="Deforestaion_Increased", hue_order=hue_order, legend=True)
ax.set_title("Figure 21: Number of Responses in each of the main Project on if Deforestation has Increased", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()

11.10 Main project and Protected Areas as a Hinderance

PAHL_grouped = df2.groupby('Name_Main_Project')['Protected_Areas_Hinderarnce_Livelihood'].value_counts(dropna=False)

PAHL_grouped1 = pd.DataFrame(PAHL_grouped)

plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = PAHL_grouped1, x="count", y="Name_Main_Project", hue="Protected_Areas_Hinderarnce_Livelihood", hue_order=hue_order, legend=True)
ax.set_title("Figure 22: Number of Responses in each of the main Project on if Protected Areas are a Hinderance to Livelihood", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()

11.11 Main Project and New Livelihood Projects

NLP_grouped = df2.groupby('Name_Main_Project')['New_Livelihood_Projects'].value_counts(dropna=False)

NLP_grouped1 = pd.DataFrame(NLP_grouped)

plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = NLP_grouped1, x="count", y="Name_Main_Project", hue="New_Livelihood_Projects", hue_order=hue_order, legend=True)
ax.set_title("Figure 23: Number of Responses in each of the main Project on if some Livelihood Subprojects not Implemented", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()

11.12 Main Project and Sustainability of Subprojects

SSC_grouped = df2.groupby('Name_Main_Project')['Subprojects_Sustainability_Contribution'].value_counts(dropna=False)

SSC_grouped1 = pd.DataFrame(SSC_grouped)

plt.figure(figsize=(8.7, 8.27))
hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax = sns.barplot(data = SSC_grouped1, x="count", y="Name_Main_Project", hue="Subprojects_Sustainability_Contribution", hue_order=hue_order, legend=True)
ax.set_title("Figure 24: Number of Responses in each of the main Project on if Subprojects Contribute to Sustainability", fontsize=14)
plt.legend(title="KEY")
for container in ax.containers:
    ax.bar_label(container, fmt="%.0f", label_type="edge", padding=3)
plt.show()
#plt.saveax("charts.png", dpi=300)

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fig = plt.figure(figsize=(30, 60))
gs = GridSpec(6, 6, figure=fig)
ax1 = fig.add_subplot(gs[0, :3])   # Row 0, columns 0-2
ax2 = fig.add_subplot(gs[0, 3:])   # Row 0, columns 3-6
ax3 = fig.add_subplot(gs[1, :3])   # Row 1, columns 0-1
ax4 = fig.add_subplot(gs[1, 3:])  # Row 1, columns 2-4
ax5 = fig.add_subplot(gs[2, :3])   # Row 1, columns 5-6
ax6 = fig.add_subplot(gs[2, 3:])   # Row 2, columns 0-2
ax7 = fig.add_subplot(gs[3, :3])   # Row 2, columns 3-6
ax8 = fig.add_subplot(gs[3, 3:])   # Row 3, columns 0-3
ax9 = fig.add_subplot(gs[4, :3])   # Row 3, columns 4-6
ax10 = fig.add_subplot(gs[4, 3:])  # Row 4, columns 0-1
ax11 = fig.add_subplot(gs[5, :3]) # Row 4, columns 2-4
ax12 = fig.add_subplot(gs[5, 3:])  # Row 4, columns 5-6

hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert", "NaN"]
ax1 = sns.barplot(data = CPH_grouped1, x="count", y="Name_Main_Project", hue="Cultural_Practices_Hinder", hue_order=hue_order, legend=True, ax=ax1)
ax1.set_title("Figure 2: Number of Responses on cultural Practices Hindering Sustainable Management of SES in the Main Projects", fontsize=14)

hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax2 = sns.barplot(data = CPC_grouped1, x="count", y="Name_Main_Project", hue="Cultural_Practices_Changed", hue_order=hue_order, legend=True, ax=ax2)
ax2.set_title("Figure 3: Number of Responses on Willingnes to Change Cultural Practices for Sustainable Management of SES in the Main Projects", fontsize=14)


hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax3 = sns.barplot(data = CAC_grouped1, x="count", y="Name_Main_Project", hue="Cultural_Aspects_Considered", hue_order=hue_order, legend=True, ax=ax3)
ax3.set_title("Figure 4: Number of Responses on if Cultural Aspects are Considered for Sustainable Management of SES ", fontsize=14)


hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax4 = sns.barplot(data = MI_grouped1, x="count", y="Name_Main_Project", hue="Measure_Indicators", hue_order=hue_order, legend=True, ax=ax4)
ax4.set_title("Figure 5: Number of Responses on if Measurement of Indicators is important for Sustainable Management of SES", fontsize=14)

hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax5 = sns.barplot(data = LD_grouped1, x="count", y="Name_Main_Project", hue="Livilihood_Depenedent", hue_order=hue_order, legend=True, ax=ax5)
ax5.set_title("Figure 6: Number of Responses in each of the main Project on if Livelihoods Depend on SES", fontsize=14)


hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax6 = sns.barplot(data = CL_grouped1, x="count", y="Name_Main_Project", hue="Change_Livelihood", hue_order=hue_order, legend=True, ax=ax6)
ax6.set_title("Figure 7: Number of Responses in each of the main Project on if Livelihoods can be Changed", fontsize=14)

hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax7 = sns.barplot(data = CLE_grouped1, x="count", y="Name_Main_Project", hue="Change_Livelihood_Easy", hue_order=hue_order, legend=True, ax=ax7)
ax7.set_title("Figure 8: Number of Responses in each of the main Project on if Livelihoods can be Changed Easily", fontsize=14)


hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax8 = sns.barplot(data = ESR_grouped1, x="count", y="Name_Main_Project", hue="Ecosystem_Services_Reduced", hue_order=hue_order, legend=True, ax=ax8)
ax8.set_title("Figure 9: Number of Responses in each of the main Project on if Ecosystem services have Reduced", fontsize=14)


hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax9 = sns.barplot(data = DI_grouped1, x="count", y="Name_Main_Project", hue="Deforestaion_Increased", hue_order=hue_order, legend=True, ax=ax9)
ax9.set_title("Figure 10: Number of Responses in each of the main Project on if Deforestation has Increased", fontsize=14)


hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax10 = sns.barplot(data = PAHL_grouped1, x="count", y="Name_Main_Project", hue="Protected_Areas_Hinderarnce_Livelihood", hue_order=hue_order, legend=True, ax=ax10)
ax10.set_title("Figure 11: Number of Responses in each of the main Project on if Protected Areas are a Hinderance to Livelihood", fontsize=14)

hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax11 = sns.barplot(data = NLP_grouped1, x="count", y="Name_Main_Project", hue="New_Livelihood_Projects", hue_order=hue_order, legend=True, ax=ax11)
ax11.set_title("Figure 12: Number of Responses in each of the main Project on if some Livelihood Subprojects not Implemented", fontsize=14)


hue_order = ["Strongly_Disagree_Likert", "Disagree_Likert", "Undecided_Likert", "Agree_Likert", "Strongly_Agree_Likert"]
ax12 = sns.barplot(data = SSC_grouped1, x="count", y="Name_Main_Project", hue="Subprojects_Sustainability_Contribution", hue_order=hue_order, legend=True, ax=ax12)
ax12.set_title("Figure 13: Number of Responses in each of the main Project on if Subprojects Contribute to Sustainability", fontsize=14)

plt.tight_layout()
plt.savefig("12charts.png", dpi=300)
plt.savefig("12graph.jpg")
plt.show()

12. Converting the Notebook

with open('02_Landscape_Transformation_Livelihood.ipynb', 'r') as f:
    notebook = nbformat.read(f, as_version=4)

# Initialize the HTML Exporter
html_exporter = HTMLExporter()
(body, resources) = html_exporter.from_notebook_node(notebook)

# Save the HTML output
with open('02_Landscape_Transformation_Livelihood.html', 'w') as f:
    f.write(body)

print("Conversion to HTML completed!")

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13. Converting to Word Document

#!pandoc 02_Landscape_Transformation_Livelihood.md -o output.docx
!pandoc 02_Landscape_Transformation_Livelihood.html -o D:/DataAnalysis/LandscapeTransformationLivelihood.docx

UTF-8 decoding error in 02_Landscape_Transformation_Livelihood.html at byte offset 243172 (95).
The input must be a UTF-8 encoded text.

#subprocess.run(["pandoc", "02_Landscape_Transformation_Livelihood.md", "-o", "02_Landscape_Transformation_Livelihood.docx"])
#subprocess.run(["pandoc", "02_Landscape_Transformation_Livelihood.md", "-o", "C:/Users/nazin/Data_Analysis/02_Landscape_Transformation.docx"])
subprocess.run(["pandoc", r"02_Landscape_Transformation_Livelihood.html", "-o", r"C:/Users/nazin/Data_Analysis/02_Landscape_Transformation.docx"], capture_output=True, text=True)
print("Conversion successful! File saved as 02_Landscape_Transformation_Livelihood.docx")

Conversion successful! File saved as 02_Landscape_Transformation_Livelihood.docx

print(shutil.which("pandoc"))

C:\Users\nazin\AppData\Local\anaconda3\envs\NLTK_Py_3_12\Scripts\pandoc.EXE